diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index f9d98ef0..f455d9e9 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -1,5 +1,15 @@
 name: CI
-on: [push]
+on:
+  # We would like to trigger for CI for any pull request action -
+  # both from QuantCo's branches as well as forks.
+  pull_request:
+  # In addition to pull requests, we want to run CI for pushes
+  # to the main branch and tags.
+  push:
+    branches:
+      - "main"
+    tags:
+      - "*"
 
 # Automatically stop old builds on the same branch/PR
 concurrency:
diff --git a/CHANGELOG.rst b/CHANGELOG.rst
index c5b1a50e..ad678eba 100644
--- a/CHANGELOG.rst
+++ b/CHANGELOG.rst
@@ -12,7 +12,21 @@ Changelog
 
 **New features**
 
-* Added :meth:`metalearners.metalearner.MetaLearner.init_params`.
+* Add :meth:`metalearners.metalearner.MetaLearner.init_params`.
+
+* Add :class:`metalearners.utils.FixedBinaryPropensity`.
+
+* Added ``_build_onnx`` to :class:`metalearners.MetaLearner` abstract class and implement it
+  for :class:`metalearners.TLearner`, :class:`metalearners.XLearner`, :class:`metalearners.RLearner`
+  and :class:`metalearners.DRLearner`.
+
+* Added ``_necessary_onnx_models`` to :class:`metalearners.MetaLearner`.
+
+* Added :meth:`metalearners.metalearner.DRLearner.average_treatment_effect` to
+  compute AIPW point estimate and standard error for _average
+  treatment effects (ATE)_
+  without requiring a full model fit (which is required for CATE
+  estimation). A new notebook contains examples.
 
 * Added :meth:`metalearners.metalearner.DRLearner.treatment_effect` to compute AIPW point estimate and standard error for _average treatment effects (ATE)_ without requiring a full model fit (which is required for CATE estimation). A new notebook contains examples.
 
@@ -21,7 +35,7 @@ Changelog
 
 **New features**
 
-* Added :meth:`metalearners.metalearner.MetaLearner.fit_all_nuisance` and
+* Add :meth:`metalearners.metalearner.MetaLearner.fit_all_nuisance` and
   :meth:`metalearners.metalearner.MetaLearner.fit_all_treatment`.
 
 * Add optional ``store_raw_results`` and ``store_results`` parameters to :class:`metalearners.grid_search.MetaLearnerGridSearch`.
diff --git a/docs/examples/example_estimating_ates.ipynb b/docs/examples/example_estimating_ates.ipynb
index 4a2c93a9..9ba10c27 100644
--- a/docs/examples/example_estimating_ates.ipynb
+++ b/docs/examples/example_estimating_ates.ipynb
@@ -14,7 +14,7 @@
     "\n",
     "Estimating average treatment effects (ATEs) involves a subset of the tasks involved in estimating Conditional Average Treatment Effects (CATEs), so we can use methods that are designed for estimating CATEs to estimate ATEs. \n",
     "\n",
-    "In this example, we simulate some data with a confounded binary treatment and demonstrate the `treatment_effect` method of the `DRLearner` class, which estimates the ATE, and compare it to estimates from some other popular libraries (`econML` and `doubleML`). We then show how this approach generalizes to a setting with multiple discrete treatments, and finally to a setting with discrete-valued outcomes.\n",
+    "In this example, we simulate some data with a confounded binary treatment and demonstrate the `average_treatment_effect` method of the `DRLearner` class, which estimates the ATE, and compare it to estimates from some other popular libraries (`econML` and `doubleML`). We then show how this approach generalizes to a setting with multiple discrete treatments, and finally to a setting with discrete-valued outcomes.\n",
     "\n",
     "Example\n",
     "-------"
@@ -236,10 +236,11 @@
     "    y=df[outcome_column],\n",
     "    w=df[treatment_column],\n",
     ")\n",
-    "metalearners_est = metalearners_dr.treatment_effect( # still need to pass data objects since DRLearner does not retain any data\n",
+    "metalearners_est = metalearners_dr.average_treatment_effect( # still need to pass data objects since DRLearner does not retain any data\n",
     "    X=df[feature_columns],\n",
     "    w=df[treatment_column],\n",
     "    y=df[outcome_column],\n",
+    "    is_oos=False,\n",
     ")\n",
     "metalearners_est"
    ]
@@ -248,7 +249,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Manual computation with pseudo outcome method produces the same estimate (`treatment_effect` does a generalisation of this under the hood) yields the same estimate"
+    "Manual computation with pseudo outcome method produces the same estimate (`average_treatment_effect` does a generalisation of this under the hood) yields the same estimate"
    ]
   },
   {
@@ -713,10 +714,11 @@
     "    y=df_multi[outcome_column],\n",
     "    w=df_multi[treatment_column],\n",
     ")\n",
-    "metalearners_est2 = metalearners_dr_2.treatment_effect( # still need to pass data objects since DRLearner does not retain any data\n",
+    "metalearners_est2 = metalearners_dr_2.average_treatment_effect( # still need to pass data objects since DRLearner does not retain any data\n",
     "    X=df_multi[feature_columns],\n",
     "    y=df_multi[outcome_column],\n",
     "    w=df_multi[treatment_column],\n",
+    "    is_oos=False,\n",
     ")\n",
     "metalearners_est2"
    ]
diff --git a/docs/examples/example_onnx.ipynb b/docs/examples/example_onnx.ipynb
new file mode 100644
index 00000000..5e2a00fd
--- /dev/null
+++ b/docs/examples/example_onnx.ipynb
@@ -0,0 +1,380 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Converting a MetaLearner to ONNX\n",
+    "\n",
+    "```{warning}\n",
+    "This is a experimental feature which is not subject to deprecation cycles. Use\n",
+    "it at your own risk!\n",
+    "```\n",
+    "\n",
+    "ONNX is an open standard for representing trained machine learning models.\n",
+    "By converting a Metalearner\n",
+    "into an ONNX model, it becomes easier to leverage the model in different environments without\n",
+    "needing to worry about compatibility or performance issues.\n",
+    "\n",
+    "In particular, this conversion also allows models to be run on a variety of hardware setups. Also, ONNX\n",
+    "models are optimized for efficient computation, enabling faster inference compared to\n",
+    "the Python interface.\n",
+    "\n",
+    "For more information about ONNX, you can check the ONNX [website](https://onnx.ai/).\n",
+    "\n",
+    "In this example we will show how most MetaLearners can be converted to ONNX.\n",
+    "\n",
+    "## Installation\n",
+    "\n",
+    "In order to convert a MetaLearner to ONNX, we first need to install the following packages:\n",
+    "\n",
+    "* [onnx](https://github.com/onnx/onnx)\n",
+    "* [onnxmltools](https://github.com/onnx/onnxmltools)\n",
+    "* [onnxruntime](https://github.com/microsoft/onnxruntime)\n",
+    "* [spox](https://github.com/Quantco/spox)\n",
+    "\n",
+    "We can do so either via conda and conda-forge:\n",
+    "\n",
+    "```console\n",
+    "$ conda install onnx onnxmltools onnxruntime spox -c conda-forge\n",
+    "```\n",
+    "\n",
+    "or via pip and PyPI\n",
+    "\n",
+    "```console\n",
+    "$ pip install onnx onnxmltools onnxruntime spox\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Usage\n",
+    "\n",
+    "```{warning}\n",
+    "It is important to notice that this method only works for {class}`~metalearners.TLearner`,\n",
+    "{class}`~metalearners.XLearner`, {class}`~metalearners.RLearner` and {class}`~metalearners.DRLearner`.\n",
+    "Converting an {class}`~metalearners.SLearner` is highly dependent on the fact that the base\n",
+    "model supports categorical variables or not and it is not implemented yet. \n",
+    "```\n",
+    "\n",
+    "### Loading the data\n",
+    "\n",
+    "Just like in our {ref}`example on estimating CATEs with a MetaLearner <example-basic>`,\n",
+    "we will first load some experiment data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from pathlib import Path\n",
+    "from git_root import git_root\n",
+    "\n",
+    "df = pd.read_csv(git_root(\"data/learning_mindset.zip\"))\n",
+    "outcome_column = \"achievement_score\"\n",
+    "treatment_column = \"intervention\"\n",
+    "feature_columns = [\n",
+    "    column for column in df.columns if column not in [outcome_column, treatment_column]\n",
+    "]\n",
+    "categorical_feature_columns = [\n",
+    "    \"ethnicity\",\n",
+    "    \"gender\",\n",
+    "    \"frst_in_family\",\n",
+    "    \"school_urbanicity\",\n",
+    "    \"schoolid\",\n",
+    "]\n",
+    "# Note that explicitly setting the dtype of these features to category\n",
+    "# allows both lightgbm as well as shap plots to\n",
+    "# 1. Operate on features which are not of type int, bool or float\n",
+    "# 2. Correctly interpret categoricals with int values to be\n",
+    "#    interpreted as categoricals, as compared to ordinals/numericals.\n",
+    "for categorical_feature_column in categorical_feature_columns:\n",
+    "    df[categorical_feature_column] = df[categorical_feature_column].astype(\"category\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that we've loaded the experiment data, we can train a MetaLearner.\n",
+    "\n",
+    "\n",
+    "### Training a MetaLearner\n",
+    "\n",
+    "Again, mirroring our {ref}`example on estimating CATEs with a MetaLearner\n",
+    "<example-basic>`, we can train an\n",
+    "{class}`~metalearners.XLearner` as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from metalearners import XLearner\n",
+    "from lightgbm import LGBMRegressor, LGBMClassifier\n",
+    "\n",
+    "xlearner = XLearner(\n",
+    "    nuisance_model_factory=LGBMRegressor,\n",
+    "    propensity_model_factory=LGBMClassifier,\n",
+    "    treatment_model_factory=LGBMRegressor,\n",
+    "    is_classification=False,\n",
+    "    n_variants=2,\n",
+    "    nuisance_model_params={\"n_estimators\": 5, \"verbose\": -1},\n",
+    "    propensity_model_params={\"n_estimators\": 5, \"verbose\": -1},\n",
+    "    treatment_model_params={\"n_estimators\": 5, \"verbose\": -1},\n",
+    "    n_folds=2,\n",
+    ")\n",
+    "\n",
+    "xlearner.fit(\n",
+    "    X=df[feature_columns],\n",
+    "    y=df[outcome_column],\n",
+    "    w=df[treatment_column],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{note}\n",
+    "In this example, we used all ``lightgbm`` models because these are the only type of models\n",
+    "that we managed to get to work with categorical encodings from ``pandas``\n",
+    "while also being convertible to ONNX. Other ``sklearn`` models which support categoricals such as\n",
+    "``HistGradientBoostingRegressor`` or ``xgboost`` models do not have support for them\n",
+    "in their conversion to ONNX. See [this issue](https://github.com/onnx/sklearn-onnx/issues/1051)\n",
+    "and [this comment](https://github.com/onnx/onnxmltools/issues/469#issuecomment-1993880910).\n",
+    "```\n",
+    "\n",
+    "### Converting the base models to ONNX\n",
+    "\n",
+    "Before being able to convert the MetaLearner to ONXX we need to manually convert the necessary\n",
+    "base models for the prediction. To get the necessary base models that need to be\n",
+    "converted we can use {meth}`~metalearners.metalearner.MetaLearner._necessary_onnx_models`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "necessary_models = xlearner._necessary_onnx_models()\n",
+    "necessary_models"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see that we need to convert the ``\"propensity_model\"``, the ``\"control_effect_model\"``\n",
+    "and the ``\"treatment_effect_model\"``. We can do this with the following code where we\n",
+    "use the ``convert_lightgbm`` function from the ``onnxmltools`` package.\n",
+    "\n",
+    "```{note}\n",
+    "It is important to know that for classifiers we need to pass the ``zipmap=False`` option. This\n",
+    "is required so the output probabilities are a Matrix and not a list of dictionaries.\n",
+    "In the case of using a ``sklearn`` model and using the ``convert_sklearn`` function, this\n",
+    "option needs to be specified with the ``options={\"zipmap\": False}`` parameter.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import onnx\n",
+    "from onnxmltools import convert_lightgbm\n",
+    "from onnxconverter_common.data_types import FloatTensorType\n",
+    "\n",
+    "onnx_models: dict[str, list[onnx.ModelProto]] = {}\n",
+    "\n",
+    "for model_kind, models in necessary_models.items():\n",
+    "    onnx_models[model_kind] = []\n",
+    "    for model in models:\n",
+    "        onnx_models[model_kind].append(\n",
+    "            convert_lightgbm(\n",
+    "                model,\n",
+    "                initial_types=[(\"X\", FloatTensorType([None, len(feature_columns)]))],\n",
+    "                zipmap=False,\n",
+    "            )\n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can call {meth}`~metalearners.metalearner.MetaLearner._build_onnx` which combines\n",
+    "the the converted ONNX base models into a single ONNX model.\n",
+    "This combined model has a single 2D input ``\"X\"`` and a single output named ``\"tau\"``.\n",
+    "The output name can be changed using the ``output_name`` parameter. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "onnx_model = xlearner._build_onnx(onnx_models)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can explore the input and output of the model and see that it expects a matrix with 11\n",
+    "columns and returns a three dimensional tensor with shape ``(..., 1, 1)`` which is expected\n",
+    "as there is only two treatment variants and one outcome as it is a regression problem."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"ONNX model input: \", onnx_model.graph.input)\n",
+    "print(\"ONNX model output: \", onnx_model.graph.output)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also visualize the ONNX model with, e.g. [netron](https://netron.app/):\n",
+    "![](onnx_netron.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{note}\n",
+    "We noticed that ``convert_lightgbm`` does not support using native pandas categorical variables.\n",
+    "This is because a numpy array needs to be passed when predicting, for this reason we need to\n",
+    "use the categories codes in the input matrix. For more context on this issue see\n",
+    "[here](https://github.com/onnx/onnxmltools/issues/309) and [here](https://github.com/microsoft/LightGBM/issues/5162).\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "X_onnx = df[feature_columns].copy(deep=True)\n",
+    "for c in categorical_feature_columns:\n",
+    "    X_onnx[c] = df[c].cat.codes\n",
+    "X_onnx = X_onnx.to_numpy(np.float32)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can finally use ``onnxruntime`` to perform predictions using our model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import onnxruntime as rt\n",
+    "\n",
+    "sess = rt.InferenceSession(\n",
+    "    onnx_model.SerializeToString(), providers=rt.get_available_providers()\n",
+    ")\n",
+    "\n",
+    "(pred_onnx,) = sess.run(\n",
+    "    [\"tau\"],\n",
+    "    {\"X\": X_onnx},\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "onnx.save_model(onnx_model, \"model.onnx\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We recommend always doing a final check with some data that the CATEs predicted by the python\n",
+    "implementation and the ONNX model are the same (up to some tolerance). This can be done with\n",
+    "the following code:\n",
+    "\n",
+    "```{note}\n",
+    "We have to use the data as if it was out-of-sample with ``oos_method = True`` as when we\n",
+    "converted the base models we used the ``_overall_estimtor``.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.testing.assert_allclose(\n",
+    "    xlearner.predict(df[feature_columns], True, \"overall\"), pred_onnx, atol=1e-6\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Further comments\n",
+    "\n",
+    "* It would be desirable to work with ``DoubleTensorType`` instead of ``FloatTensorType``\n",
+    "  but we have noted that some converters have issues with it. We recommend try using\n",
+    "  ``DoubleTensorType`` but switching to ``FloatTensorType`` in case the converter fails.\n",
+    "* In the case the final assertion fails we recommend first testing that the different\n",
+    "  base models have the same base outputs as we discovered some issues with some converters,\n",
+    "  see [this issue](https://github.com/onnx/sklearn-onnx/issues/1117) and\n",
+    "  [this issue](https://github.com/onnx/sklearn-onnx/issues/1116)."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/examples/example_propensity.ipynb b/docs/examples/example_propensity.ipynb
index 3a356351..7fc280f8 100644
--- a/docs/examples/example_propensity.ipynb
+++ b/docs/examples/example_propensity.ipynb
@@ -59,8 +59,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Creating our own estimator\n",
-    "--------------------------\n",
+    "Using a dummy estimator\n",
+    "-----------------------\n",
     "\n",
     "In this tutorial we will assume that we know that all observations were assigned to the\n",
     "treatment with a fixed probability of 0.3, which is close to the fraction of the observations\n",
@@ -89,43 +89,10 @@
     "dataset, we just use it for illustrational purposes.\n",
     "```\n",
     "\n",
-    "Now we can define our custom ``sklearn``-like classifier. We recommend inheriting from\n",
-    "the ``sklearn`` base classes and following the rules explained in the\n",
-    "[sklearn documentation](https://scikit-learn.org/stable/developers/develop.html) to avoid\n",
-    "having to define helper functions and ensure the correct functionality of the ``metalearners``\n",
-    "library."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "vscode": {
-     "languageId": "plaintext"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from sklearn.base import BaseEstimator, ClassifierMixin\n",
-    "from typing import Any\n",
-    "from typing_extensions import Self\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "\n",
-    "\n",
-    "class FixedPropensityModel(ClassifierMixin, BaseEstimator):\n",
-    "    def __init__(self, propensity_score: float) -> None:\n",
-    "        self.propensity_score = propensity_score\n",
-    "\n",
-    "    def fit(self, X: pd.DataFrame, y: pd.Series) -> Self:\n",
-    "        self.classes_ = np.unique(y.to_numpy())  # sklearn requires this\n",
-    "        return self\n",
-    "\n",
-    "    def predict(self, X: pd.DataFrame) -> np.ndarray[Any, Any]:\n",
-    "        return np.argmax(self.predict_proba(X), axis=1)\n",
-    "\n",
-    "    def predict_proba(self, X: pd.DataFrame) -> np.ndarray[Any, Any]:\n",
-    "        return np.full((len(X), 2), [1 - self.propensity_score, self.propensity_score])"
+    "Now we can use a custom ``sklearn``-like classifier: {class}`~metalearners.utils.FixedBinaryPropensity`.\n",
+    "The latter can be used like any ``sklearn`` classifier but will always return the same propensity,\n",
+    "independently of the observed covariates. This propensity has to be provided at initialization via the\n",
+    "``propensity_score`` parameter."
    ]
   },
   {
@@ -149,11 +116,12 @@
    "outputs": [],
    "source": [
     "from metalearners import RLearner\n",
+    "from metalearners.utils import FixedBinaryPropensity\n",
     "from lightgbm import LGBMRegressor\n",
     "\n",
     "rlearner = RLearner(\n",
     "    nuisance_model_factory=LGBMRegressor,\n",
-    "    propensity_model_factory=FixedPropensityModel,\n",
+    "    propensity_model_factory=FixedBinaryPropensity,\n",
     "    treatment_model_factory=LGBMRegressor,\n",
     "    nuisance_model_params={\"verbose\": -1},\n",
     "    propensity_model_params={\"propensity_score\": 0.3},\n",
@@ -205,10 +173,24 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/docs/examples/index.rst b/docs/examples/index.rst
index 2ef14398..b91cea15 100644
--- a/docs/examples/index.rst
+++ b/docs/examples/index.rst
@@ -15,3 +15,4 @@ Examples
    Generating data <example_data_generation.ipynb>
    Estimating CATEs for survival analysis <example_survival.ipynb>
    What if I know the propensity score? <example_propensity.ipynb>
+   Converting a MetaLearner to ONNX <example_onnx.ipynb>
diff --git a/docs/examples/onnx_netron.png b/docs/examples/onnx_netron.png
new file mode 100644
index 00000000..56efd3c7
Binary files /dev/null and b/docs/examples/onnx_netron.png differ
diff --git a/metalearners/_utils.py b/metalearners/_utils.py
index 2af472fe..2337c421 100644
--- a/metalearners/_utils.py
+++ b/metalearners/_utils.py
@@ -1,6 +1,7 @@
 # Copyright (c) QuantCo 2024-2024
 # SPDX-License-Identifier: BSD-3-Clause
 
+import warnings
 from collections.abc import Callable
 from inspect import signature
 from operator import le, lt
@@ -18,7 +19,7 @@
 
 _PREDICT = "predict"
 _PREDICT_PROBA = "predict_proba"
-
+ONNX_PROBABILITIES_OUTPUTS = ["probabilities", "output_probability"]
 
 default_rng = np.random.default_rng()
 
@@ -499,3 +500,67 @@ def default_metric(predict_method: PredictMethod) -> str:
     if predict_method == _PREDICT_PROBA:
         return "neg_log_loss"
     return "neg_root_mean_squared_error"
+
+
+def check_onnx_installed() -> None:
+    """Ensures that ``onnx`` is available."""
+    try:
+        import onnx  # noqa F401
+    except ImportError:
+        raise ImportError(
+            "onnx is not installed. Please install onnx to use this feature."
+        )
+
+
+def check_spox_installed() -> None:
+    """Ensures that ``spox`` is available."""
+    try:
+        import spox  # noqa F401
+    except ImportError:
+        raise ImportError(
+            "spox is not installed. Please install spox to use this feature."
+        )
+
+
+def infer_dtype_and_shape_onnx(tensor) -> tuple[np.dtype, tuple]:
+    """Returns the ``np.dtype`` and shape of an ONNX tensor."""
+    check_onnx_installed()
+    import onnx
+
+    dtype = onnx.helper.tensor_dtype_to_np_dtype(tensor.type.tensor_type.elem_type)
+    shape = tuple(
+        d.dim_value if d.HasField("dim_value") else None
+        for d in tensor.type.tensor_type.shape.dim
+    )
+    return dtype, shape
+
+
+def infer_probabilities_output(model) -> tuple[int, str]:
+    """Returns the index and name of the output which contains the probabilities outcome
+    in a ONNX classifier."""
+    check_onnx_installed()
+    for i, output in enumerate(model.graph.output):
+        if output.name in ONNX_PROBABILITIES_OUTPUTS:
+            return i, output.name
+    raise ValueError("No probabilities output was found.")
+
+
+def infer_input_dict(model) -> dict:
+    """Returns a dict where the keys are the input names of the model and the values are
+    ``spox.Var`` with the corresponding shape and type."""
+    check_spox_installed()
+    from spox import Tensor, Var, argument
+
+    input_dict: dict[str, Var] = {}
+    for input_tensor in model.graph.input:
+        input_dtype, input_shape = infer_dtype_and_shape_onnx(input_tensor)
+        input_dict[input_tensor.name] = argument(Tensor(input_dtype, input_shape))
+
+    return input_dict
+
+
+def warning_experimental_feature(function_name: str) -> None:
+    warnings.warn(
+        f"{function_name} is an experimental feature. Use it at your own risk!",
+        stacklevel=2,
+    )
diff --git a/metalearners/drlearner.py b/metalearners/drlearner.py
index 18df8af8..1bfc2ab5 100644
--- a/metalearners/drlearner.py
+++ b/metalearners/drlearner.py
@@ -2,6 +2,8 @@
 # SPDX-License-Identifier: BSD-3-Clause
 
 
+from collections.abc import Mapping, Sequence
+
 import numpy as np
 from joblib import Parallel, delayed
 from typing_extensions import Any, Self
@@ -18,12 +20,16 @@
     _ScikitModel,
 )
 from metalearners._utils import (
+    check_spox_installed,
     clip_element_absolute_value_to_epsilon,
+    copydoc,
     get_one,
     get_predict,
     get_predict_proba,
     index_matrix,
+    infer_input_dict,
     validate_valid_treatment_variant_not_control,
+    warning_experimental_feature,
 )
 from metalearners.cross_fit_estimator import OVERALL, CrossFitEstimator
 from metalearners.metalearner import (
@@ -38,6 +44,7 @@
     _fit_cross_fit_estimator_joblib,
     _ModelSpecifications,
     _ParallelJoblibSpecification,
+    get_overall_estimators,
 )
 
 _EPSILON = 1e-09
@@ -331,11 +338,12 @@ def evaluate(
 
         return variant_outcome_evaluation | propensity_evaluation | treatment_evaluation
 
-    def treatment_effect(
+    def average_treatment_effect(
         self,
         X: Matrix,
         y: Vector,
         w: Vector,
+        is_oos: bool,
     ) -> np.ndarray:
         """Compute Average Treatment Effect (ATE) for each treatment variant using the
         Augmented IPW estimator (Robins et al 1994). Does not require fitting a second-
@@ -344,9 +352,10 @@ def treatment_effect(
         :meth:`~metalearners.drlearner.DRLearner.fit_all_nuisance` method.
 
         Args:
-            X (Matrix): Covariate matrix.
-            y (Vector): Outcome vector.
+            X (Matrix): Covariate matrix
+            y (Vector): Outcome vector
             w (Vector): Treatment vector
+            is_oos (bool): indicator whether data is out of sample
 
         Returns:
             np.ndarray: Treatment effect and standard error for each treatment variant.
@@ -362,7 +371,7 @@ def treatment_effect(
                 w=w,
                 y=y,
                 treatment_variant=treatment_variant,
-                is_oos=False,
+                is_oos=is_oos,
             )
         treatment_effect = gamma_matrix.mean(axis=0)
         standard_error = gamma_matrix.std(axis=0) / np.sqrt(len(X))
@@ -439,3 +448,46 @@ def _pseudo_outcome(
     @property
     def init_args(self) -> dict[str, Any]:
         return super().init_args | {"adaptive_clipping": self.adaptive_clipping}
+
+    def _necessary_onnx_models(self) -> dict[str, list[_ScikitModel]]:
+        return {
+            TREATMENT_MODEL: get_overall_estimators(
+                self._treatment_models[TREATMENT_MODEL]
+            )
+        }
+
+    @copydoc(MetaLearner._build_onnx, sep="")
+    def _build_onnx(self, models: Mapping[str, Sequence], output_name: str = "tau"):
+        """In the DRLearner case, the necessary models are:
+
+        * ``"treatment_model"``
+        """
+        warning_experimental_feature("_build_onnx")
+        check_spox_installed()
+        import spox.opset.ai.onnx.v21 as op
+        from onnx.checker import check_model
+        from spox import Var, build, inline
+
+        self._validate_feature_set_none()
+        self._validate_onnx_models(models, set(self._necessary_onnx_models().keys()))
+
+        input_dict = infer_input_dict(models[TREATMENT_MODEL][0])
+
+        treatment_output_name = models[TREATMENT_MODEL][0].graph.output[0].name
+
+        tau_hat: list[Var] = []
+        for m in models[TREATMENT_MODEL]:
+            tau_hat_tv = inline(m)(**input_dict)[treatment_output_name]
+            tau_hat_tv = op.unsqueeze(tau_hat_tv, axes=op.constant(value_int=2))
+            if self.is_classification:
+                if self._supports_multi_class():
+                    raise ValueError(
+                        "ONNX conversion is not implemented for a multi-class output."
+                    )
+                tau_hat_tv = op.concat([op.neg(tau_hat_tv), tau_hat_tv], axis=-1)
+            tau_hat.append(tau_hat_tv)
+
+        cate = op.concat(tau_hat, axis=1)
+        final_model = build(input_dict, {output_name: cate})
+        check_model(final_model, full_check=True)
+        return final_model
diff --git a/metalearners/metalearner.py b/metalearners/metalearner.py
index 5bed116a..44b3aea9 100644
--- a/metalearners/metalearner.py
+++ b/metalearners/metalearner.py
@@ -2,7 +2,7 @@
 # SPDX-License-Identifier: BSD-3-Clause
 
 from abc import ABC, abstractmethod
-from collections.abc import Callable, Collection, Sequence
+from collections.abc import Callable, Collection, Mapping, Sequence, Set
 from copy import deepcopy
 from dataclasses import dataclass
 from typing import Any, TypedDict
@@ -27,6 +27,7 @@
     _ScikitModel,
 )
 from metalearners._utils import (
+    ONNX_PROBABILITIES_OUTPUTS,
     default_metric,
     index_matrix,
     validate_model_and_predict_method,
@@ -137,6 +138,17 @@ def _validate_n_folds_synchronize(n_folds: dict[str, int]) -> None:
         raise ValueError("Need at least two folds to use synchronization.")
 
 
+def get_overall_estimators(cfes: list[CrossFitEstimator]) -> list[_ScikitModel]:
+    overall_estimators = []
+    for cfe in cfes:
+        if cfe._overall_estimator is None:
+            raise ValueError(
+                "To use this functionality the overall models need to be fitted."
+            )
+        overall_estimators.append(cfe._overall_estimator)
+    return overall_estimators
+
+
 def _evaluate_model_kind(
     cfes: Sequence[CrossFitEstimator],
     Xs: Sequence[Matrix],
@@ -1172,6 +1184,98 @@ def init_args(self) -> dict[str, Any]:
             "random_state": self.random_state,
         }
 
+    def _validate_onnx_models(
+        self, models: Mapping[str, Sequence], necessary_models: Set[str]
+    ):
+        """Validates that the converted ONNX models are correct.
+
+        Specifically it validates the following:
+        * The ``necessary_models`` are equal to the keys of the ``models``` dictionary
+        * The number of models for each model matches the cardinality in the MetaLearner
+        * All ONNX have the same input format
+        * The models with ``"predict"`` as ``predict_method`` have only one output
+        * The models with ``"predict_proba"`` as ``predict_method`` have a probabilities output
+        """
+        if set(models.keys()) != necessary_models:
+            raise ValueError(
+                f"necessary_model ({necessary_models}) should equal to keys present in models dictionary."
+            )
+        specifications = (
+            self.nuisance_model_specifications() | self.treatment_model_specifications()
+        )
+        input_format = None
+        for model_kind in necessary_models:
+            model_specification = specifications[model_kind]
+            if len(models[model_kind]) != model_specification["cardinality"](self):
+                raise ValueError(
+                    f"{model_kind} cardinality does not match the expected cardinality."
+                )
+            predict_method = model_specification["predict_method"](self)
+            for model_index, model in enumerate(models[model_kind]):
+                if input_format is None:
+                    input_format = model.graph.input
+                elif input_format != model.graph.input:
+                    raise ValueError(
+                        "Some ONNX model has a different input, check that all models have "
+                        "the same input format."
+                    )
+                if predict_method == "predict" and len(model.graph.output) != 1:
+                    raise ValueError(
+                        f"ONNX {model_kind} with index {model_index} has {len(model.graph.output)} "
+                        "outputs and should have only one."
+                    )
+                elif predict_method == "predict_proba":
+                    found_probabilities = False
+                    for output in model.graph.output:
+                        if output.name in ONNX_PROBABILITIES_OUTPUTS:
+                            found_probabilities = True
+                    if not found_probabilities:
+                        raise ValueError(
+                            f"ONNX {model_kind} model with index {model_index} needs to have an output "
+                            f"with name in {ONNX_PROBABILITIES_OUTPUTS}."
+                        )
+
+    def _validate_feature_set_none(self):
+        """Validates that the feature set for all the models is None, i.e. all models
+        use all features."""
+        for feature_set in self.feature_set.values():
+            if feature_set is not None:
+                raise ValueError(
+                    "ONNX conversion can only be used if all base models have ``None`` "
+                    "as feature set (and therefore use all the features)."
+                )
+
+    @abstractmethod
+    def _necessary_onnx_models(self) -> dict[str, list[_ScikitModel]]:
+        """Return a dictionary with the necessary models to convert the MetaLearner to
+        ONNX.
+
+        The returned dictionary keys will be strings and the values will be list of the
+        overall base models (trained on the complete dataset) which should be converted
+        to onnx.
+        """
+        ...
+
+    @abstractmethod
+    def _build_onnx(self, models: Mapping[str, Sequence], output_name: str = "tau"):
+        """Convert the MetaLearner to an ONNX model.
+
+        .. warning::
+            This is a experimental feature which is not subject to deprecation cycles. Use
+            it at your own risk!
+
+        ``output_name`` can be used to change the output name of the ONNX model.
+
+        ``models`` should be a dictionary of sequences with the necessary base models converted to
+        ONNX. The necessary models for the specific MetaLearner can be accessed with
+        :meth:`~metalearners.metalearner.MetaLearner._necessary_onnx_models`.
+
+        This method combines the the converted ONNX base models into a single ONNX model.
+        This combined model has a single 2D input ``"X"`` and a single output named
+        ``output_name``.
+        """
+        ...
+
 
 class _ConditionalAverageOutcomeMetaLearner(MetaLearner, ABC):
 
diff --git a/metalearners/rlearner.py b/metalearners/rlearner.py
index 965b928c..a43ff8ea 100644
--- a/metalearners/rlearner.py
+++ b/metalearners/rlearner.py
@@ -2,13 +2,16 @@
 # SPDX-License-Identifier: BSD-3-Clause
 
 
+from collections.abc import Mapping, Sequence
+
 import numpy as np
 from joblib import Parallel, delayed
 from sklearn.metrics import root_mean_squared_error
 from typing_extensions import Self
 
-from metalearners._typing import Matrix, OosMethod, Scoring, Vector
+from metalearners._typing import Matrix, OosMethod, Scoring, Vector, _ScikitModel
 from metalearners._utils import (
+    check_spox_installed,
     clip_element_absolute_value_to_epsilon,
     copydoc,
     function_has_argument,
@@ -16,8 +19,10 @@
     get_predict,
     get_predict_proba,
     index_matrix,
+    infer_input_dict,
     validate_all_vectors_same_index,
     validate_valid_treatment_variant_not_control,
+    warning_experimental_feature,
 )
 from metalearners.cross_fit_estimator import OVERALL
 from metalearners.metalearner import (
@@ -30,6 +35,7 @@
     _fit_cross_fit_estimator_joblib,
     _ModelSpecifications,
     _ParallelJoblibSpecification,
+    get_overall_estimators,
 )
 
 OUTCOME_MODEL = "outcome_model"
@@ -519,3 +525,46 @@ def _pseudo_outcome_and_weights(
         weights = np.square(w_residuals)
 
         return pseudo_outcomes, weights
+
+    def _necessary_onnx_models(self) -> dict[str, list[_ScikitModel]]:
+        return {
+            TREATMENT_MODEL: get_overall_estimators(
+                self._treatment_models[TREATMENT_MODEL]
+            )
+        }
+
+    @copydoc(MetaLearner._build_onnx, sep="")
+    def _build_onnx(self, models: Mapping[str, Sequence], output_name: str = "tau"):
+        """In the RLearner case, the necessary models are:
+
+        * ``"treatment_model"``
+        """
+        warning_experimental_feature("_build_onnx")
+        check_spox_installed()
+        import spox.opset.ai.onnx.v21 as op
+        from onnx.checker import check_model
+        from spox import Var, build, inline
+
+        self._validate_feature_set_none()
+        self._validate_onnx_models(models, set(self._necessary_onnx_models().keys()))
+
+        input_dict = infer_input_dict(models[TREATMENT_MODEL][0])
+
+        treatment_output_name = models[TREATMENT_MODEL][0].graph.output[0].name
+
+        tau_hats: list[Var] = []
+        for m in models[TREATMENT_MODEL]:
+            tau_hat_tv = inline(m)(**input_dict)[treatment_output_name]
+            tau_hat_tv = op.unsqueeze(tau_hat_tv, axes=op.constant(value_int=2))
+            if self.is_classification:
+                if self._supports_multi_class():
+                    raise ValueError(
+                        "ONNX conversion is not implemented for a multi-class output."
+                    )
+                tau_hat_tv = op.concat([op.neg(tau_hat_tv), tau_hat_tv], axis=-1)
+            tau_hats.append(tau_hat_tv)
+
+        tau_hat = op.concat(tau_hats, axis=1)
+        final_model = build(input_dict, {output_name: tau_hat})
+        check_model(final_model, full_check=True)
+        return final_model
diff --git a/metalearners/slearner.py b/metalearners/slearner.py
index 3e1d8078..e636c0f2 100644
--- a/metalearners/slearner.py
+++ b/metalearners/slearner.py
@@ -2,6 +2,7 @@
 # SPDX-License-Identifier: BSD-3-Clause
 
 import warnings
+from collections.abc import Mapping, Sequence
 
 import numpy as np
 import pandas as pd
@@ -297,3 +298,14 @@ def predict_conditional_average_outcomes(
         return np.stack(conditional_average_outcomes_list, axis=1).reshape(
             n_obs, self.n_variants, -1
         )
+
+    @classmethod
+    def _necessary_onnx_models(cls) -> dict[str, list[_ScikitModel]]:
+        raise ValueError(
+            "The SLearner does not implement this method. Please refer to comment in the tutorial."
+        )
+
+    def _build_onnx(self, models: Mapping[str, Sequence], output_name: str = "tau"):
+        raise ValueError(
+            "The SLearner does not implement this method. Please refer to comment in the tutorial."
+        )
diff --git a/metalearners/tlearner.py b/metalearners/tlearner.py
index d512ab12..c009e597 100644
--- a/metalearners/tlearner.py
+++ b/metalearners/tlearner.py
@@ -1,12 +1,21 @@
 # Copyright (c) QuantCo 2024-2024
 # SPDX-License-Identifier: BSD-3-Clause
 
+from collections.abc import Mapping, Sequence
+
 import numpy as np
 from joblib import Parallel, delayed
 from typing_extensions import Self
 
-from metalearners._typing import Matrix, OosMethod, Scoring, Vector
-from metalearners._utils import index_matrix
+from metalearners._typing import Matrix, OosMethod, Scoring, Vector, _ScikitModel
+from metalearners._utils import (
+    check_spox_installed,
+    copydoc,
+    index_matrix,
+    infer_input_dict,
+    infer_probabilities_output,
+    warning_experimental_feature,
+)
 from metalearners.cross_fit_estimator import OVERALL
 from metalearners.metalearner import (
     NUISANCE,
@@ -17,6 +26,7 @@
     _fit_cross_fit_estimator_joblib,
     _ModelSpecifications,
     _ParallelJoblibSpecification,
+    get_overall_estimators,
 )
 
 
@@ -140,3 +150,53 @@ def evaluate(
             is_treatment_model=False,
             feature_set=self.feature_set[VARIANT_OUTCOME_MODEL],
         )
+
+    def _necessary_onnx_models(self) -> dict[str, list[_ScikitModel]]:
+        return {
+            VARIANT_OUTCOME_MODEL: get_overall_estimators(
+                self._nuisance_models[VARIANT_OUTCOME_MODEL]
+            )
+        }
+
+    @copydoc(MetaLearner._build_onnx, sep="")
+    def _build_onnx(self, models: Mapping[str, Sequence], output_name: str = "tau"):
+        """In the TLearner case, the necessary models are:
+
+        * ``"variant_outcome_model"``
+        """
+        warning_experimental_feature("_build_onnx")
+        check_spox_installed()
+        import spox.opset.ai.onnx.v21 as op
+        from onnx.checker import check_model
+        from spox import build, inline
+
+        self._validate_feature_set_none()
+        self._validate_onnx_models(models, set(self._necessary_onnx_models().keys()))
+
+        input_dict = infer_input_dict(models[VARIANT_OUTCOME_MODEL][0])
+
+        if not self.is_classification:
+            model_output_name = models[VARIANT_OUTCOME_MODEL][0].graph.output[0].name
+        else:
+            _, model_output_name = infer_probabilities_output(
+                models[VARIANT_OUTCOME_MODEL][0]
+            )
+
+        output_0 = inline(models[VARIANT_OUTCOME_MODEL][0])(**input_dict)[
+            model_output_name
+        ]
+
+        variant_cates = []
+        for m in models[VARIANT_OUTCOME_MODEL][1:]:
+            variant_output = inline(m)(**input_dict)[model_output_name]
+            variant_cate = op.sub(variant_output, output_0)
+            variant_cates.append(
+                op.unsqueeze(
+                    variant_cate,
+                    axes=op.constant(value_int=1),
+                )
+            )
+        cate = op.concat(variant_cates, axis=1)
+        final_model = build(input_dict, {output_name: cate})
+        check_model(final_model, full_check=True)
+        return final_model
diff --git a/metalearners/utils.py b/metalearners/utils.py
index 765e6c1d..587bef67 100644
--- a/metalearners/utils.py
+++ b/metalearners/utils.py
@@ -1,8 +1,14 @@
 # Copyright (c) QuantCo 2024-2024
 # SPDX-License-Identifier: BSD-3-Clause
 
+from typing import Any
+
 import numpy as np
+import pandas as pd
+from sklearn.base import BaseEstimator, ClassifierMixin
+from typing_extensions import Self
 
+from metalearners._typing import Matrix, Vector
 from metalearners.drlearner import DRLearner
 from metalearners.metalearner import MetaLearner
 from metalearners.rlearner import RLearner
@@ -73,3 +79,29 @@ def simplify_output(tensor: np.ndarray) -> np.ndarray:
     if n_outputs == 2:
         return tensor[:, :, 1].reshape(n_obs, n_variants)
     return tensor
+
+
+class FixedBinaryPropensity(ClassifierMixin, BaseEstimator):
+    """Binary classifier propensity dummy model which outputs a fixed propensity,
+    independently of covariates."""
+
+    def __init__(self, propensity_score: float) -> None:
+        if not 0 <= propensity_score <= 1:
+            raise ValueError(
+                f"Expected a propensity score between 0 and 1 but got {propensity_score}."
+            )
+        self.propensity_score = propensity_score
+
+    def fit(self, X: Matrix, y: Vector) -> Self:
+        self.classes_ = np.unique(y)  # sklearn requires this
+        if (n_classes := len(self.classes_)) > 2:
+            raise ValueError(
+                f"FixedBinaryPropensityModel only supports binary outcomes but {n_classes} were provided ."
+            )
+        return self
+
+    def predict(self, X: Matrix) -> np.ndarray[Any, Any]:
+        return np.argmax(self.predict_proba(X), axis=1)
+
+    def predict_proba(self, X: pd.DataFrame) -> np.ndarray[Any, Any]:
+        return np.full((len(X), 2), [1 - self.propensity_score, self.propensity_score])
diff --git a/metalearners/xlearner.py b/metalearners/xlearner.py
index 7e8a1b21..5965b96c 100644
--- a/metalearners/xlearner.py
+++ b/metalearners/xlearner.py
@@ -2,17 +2,24 @@
 # SPDX-License-Identifier: BSD-3-Clause
 
 
+from collections.abc import Mapping, Sequence
+
 import numpy as np
 from joblib import Parallel, delayed
 from typing_extensions import Self
 
-from metalearners._typing import Matrix, OosMethod, Scoring, Vector
+from metalearners._typing import Matrix, OosMethod, Scoring, Vector, _ScikitModel
 from metalearners._utils import (
+    check_spox_installed,
+    copydoc,
     get_one,
     get_predict,
     get_predict_proba,
     index_matrix,
+    infer_input_dict,
+    infer_probabilities_output,
     validate_valid_treatment_variant_not_control,
+    warning_experimental_feature,
 )
 from metalearners.cross_fit_estimator import MEDIAN, OVERALL
 from metalearners.metalearner import (
@@ -26,6 +33,7 @@
     _fit_cross_fit_estimator_joblib,
     _ModelSpecifications,
     _ParallelJoblibSpecification,
+    get_overall_estimators,
 )
 
 CONTROL_EFFECT_MODEL = "control_effect_model"
@@ -430,3 +438,90 @@ def _pseudo_outcome(
         imputed_te_control = treatment_outcome - y_filt[control_indices]
 
         return imputed_te_control, imputed_te_treatment
+
+    def _necessary_onnx_models(self) -> dict[str, list[_ScikitModel]]:
+        return {
+            PROPENSITY_MODEL: get_overall_estimators(
+                self._nuisance_models[PROPENSITY_MODEL]
+            ),
+            CONTROL_EFFECT_MODEL: get_overall_estimators(
+                self._treatment_models[CONTROL_EFFECT_MODEL]
+            ),
+            TREATMENT_EFFECT_MODEL: get_overall_estimators(
+                self._treatment_models[TREATMENT_EFFECT_MODEL]
+            ),
+        }
+
+    @copydoc(MetaLearner._build_onnx, sep="")
+    def _build_onnx(self, models: Mapping[str, Sequence], output_name: str = "tau"):
+        """In the XLearner case, the necessary models are:
+
+        * ``"propensity_model"``
+        * ``"control_effect_model"``
+        * ``"treatment_effect_model"``
+        """
+        warning_experimental_feature("_build_onnx")
+        check_spox_installed()
+        import spox.opset.ai.onnx.v21 as op
+        from onnx.checker import check_model
+        from spox import Var, build, inline
+
+        self._validate_feature_set_none()
+        self._validate_onnx_models(models, set(self._necessary_onnx_models().keys()))
+
+        input_dict = infer_input_dict(models[PROPENSITY_MODEL][0])
+
+        treatment_output_name = models[CONTROL_EFFECT_MODEL][0].graph.output[0].name
+
+        tau_hat_control: list[Var] = []
+        for m in models[CONTROL_EFFECT_MODEL]:
+            tau_hat_control.append(inline(m)(**input_dict)[treatment_output_name])
+        tau_hat_effect: list[Var] = []
+        for m in models[TREATMENT_EFFECT_MODEL]:
+            tau_hat_effect.append(inline(m)(**input_dict)[treatment_output_name])
+
+        _, propensity_output_name = infer_probabilities_output(
+            models[PROPENSITY_MODEL][0]
+        )
+
+        propensity_scores = inline(models[PROPENSITY_MODEL][0])(**input_dict)[
+            propensity_output_name
+        ]
+
+        slice_0 = op.slice(
+            propensity_scores,
+            starts=op.const(np.array([0])),
+            ends=op.const(np.array([1])),
+            axes=op.const(np.array([1])),
+        )
+
+        tau_hat = []
+        for tv in range(self.n_variants - 1):
+            slice_tv = op.slice(
+                propensity_scores,
+                starts=op.const(np.array([tv + 1])),
+                ends=op.const(np.array([tv + 2])),
+                axes=op.const(np.array([1])),
+            )
+            denominator = op.add(slice_0, slice_tv)
+            scaled_propensity = op.div(slice_tv, denominator)
+            tau_hat_tv = op.add(
+                op.mul(scaled_propensity, tau_hat_control[tv]),
+                op.mul(
+                    op.sub(op.constant(value_float=1), scaled_propensity),
+                    tau_hat_effect[tv],
+                ),
+            )
+            tau_hat_tv = op.unsqueeze(tau_hat_tv, axes=op.constant(value_int=2))
+            if self.is_classification:
+                if self._supports_multi_class():
+                    raise ValueError(
+                        "ONNX conversion is not implemented for a multi-class output."
+                    )
+                tau_hat_tv = op.concat([op.neg(tau_hat_tv), tau_hat_tv], axis=-1)
+            tau_hat.append(tau_hat_tv)
+
+        cate = op.concat(tau_hat, axis=1)
+        final_model = build(input_dict, {output_name: cate})
+        check_model(final_model, full_check=True)
+        return final_model
diff --git a/pixi.lock b/pixi.lock
index 7227f074..cb9aa727 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -13,7 +13,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-bin-1.1.0-hd590300_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.7.4-hbcca054_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.0-hbb29018_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.0-hebfffa5_3.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/causalml-0.15.1-py310h2bcf4ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2024.7.4-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cloudpickle-3.0.0-pyhd8ed1ab_0.conda
@@ -40,7 +40,6 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/fsspec-2024.6.1-pyhff2d567_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/future-1.0.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/gdk-pixbuf-2.42.12-hb9ae30d_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/giflib-5.2.2-hd590300_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/git_root-0.1-py_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/gmp-6.3.0-hac33072_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/gmpy2-2.1.5-py310hc7909c9_1.conda
@@ -48,8 +47,8 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/graphviz-12.0.0-hba01fac_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/gtk2-2.24.33-h6470451_5.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/gts-0.7.6-h977cf35_4.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-9.0.0-hfac3d4d_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/icu-73.2-h59595ed_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-9.0.0-hda332d3_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/icu-75.1-he02047a_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.4-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/kiwisolver-1.4.5-py310hd41b1e2_1.conda
@@ -58,7 +57,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/lerc-4.0.0-h27087fc_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libabseil-20240116.2-cxx17_he02047a_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-23_linux64_openblas.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libboost-1.84.0-hba137d9_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libboost-1.84.0-h0ccab89_5.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.1.0-hd590300_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libbrotlidec-1.1.0-hd590300_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libbrotlienc-1.1.0-hd590300_1.conda
@@ -67,7 +66,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.6.2-h59595ed_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libffi-3.4.2-h7f98852_5.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-14.1.0-h77fa898_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgd-2.3.3-h119a65a_9.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgd-2.3.3-hd3e95f3_10.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-14.1.0-h69a702a_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-14.1.0-hc5f4f2c_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libglib-2.80.3-h8a4344b_1.conda
@@ -87,11 +86,10 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libtorch-2.3.1-cpu_generic_h970db74_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.38.1-h0b41bf4_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libuv-1.48.0-hd590300_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libwebp-1.4.0-h2c329e2_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.4.0-hd590300_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libxcb-1.16-hd590300_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libxgboost-2.1.1-cpu_h7afb2cf_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.12.7-h4c95cb1_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.12.7-he7c6b58_4.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-h4ab18f5_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/lightgbm-4.5.0-cpu_py3.10h77395be_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/llvmlite-0.43.0-py310h4c7c693_0.conda
@@ -183,7 +181,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/osx-64/brotli-bin-1.1.0-h0dc2134_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/bzip2-1.0.8-hfdf4475_7.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/ca-certificates-2024.7.4-h8857fd0_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-64/cairo-1.18.0-h9f650ed_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-64/cairo-1.18.0-h37bd5c4_3.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/causalml-0.15.1-py310hc47d138_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2024.7.4-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cloudpickle-3.0.0-pyhd8ed1ab_0.conda
@@ -210,7 +208,6 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/fsspec-2024.6.1-pyhff2d567_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/future-1.0.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/gdk-pixbuf-2.42.12-ha587570_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-64/giflib-5.2.2-h10d778d_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/git_root-0.1-py_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/osx-64/gmp-6.3.0-hf036a51_2.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/gmpy2-2.1.5-py310h0310db1_1.conda
@@ -218,8 +215,8 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/osx-64/graphviz-12.0.0-he14ced1_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/gtk2-2.24.33-h2c15c3c_5.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/gts-0.7.6-h53e17e3_4.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-64/harfbuzz-9.0.0-h053f038_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-64/icu-73.2-hf5e326d_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-64/harfbuzz-9.0.0-h098a298_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-64/icu-75.1-h120a0e1_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.4-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/khronos-opencl-icd-loader-2023.04.17-h37ebe6b_1.conda
@@ -228,16 +225,16 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/osx-64/lerc-4.0.0-hb486fe8_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/osx-64/libabseil-20240116.2-cxx17_hf036a51_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/libblas-3.9.0-22_osx64_openblas.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-64/libboost-1.84.0-h739af76_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-64/libboost-1.84.0-hcca3243_5.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/libbrotlicommon-1.1.0-h0dc2134_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/libbrotlidec-1.1.0-h0dc2134_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/libbrotlienc-1.1.0-h0dc2134_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/libcblas-3.9.0-22_osx64_openblas.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-64/libcxx-18.1.8-hef8daea_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-64/libcxx-18.1.8-heced48a_2.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/libdeflate-1.21-hfdf4475_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/libexpat-2.6.2-h73e2aa4_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/libffi-3.4.2-h0d85af4_5.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/osx-64/libgd-2.3.3-h0dceb68_9.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-64/libgd-2.3.3-h2e77e4f_10.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/libgfortran-5.0.0-13_2_0_h97931a8_3.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/libgfortran5-13.2.0-h2873a65_3.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/libglib-2.80.3-h736d271_1.conda
@@ -255,11 +252,10 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/osx-64/libtiff-4.6.0-h603087a_4.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/libtorch-2.3.1-cpu_mkl_hcc88766_101.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/libuv-1.48.0-h67532ce_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-64/libwebp-1.4.0-hc207709_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/libwebp-base-1.4.0-h10d778d_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/libxcb-1.16-h0dc2134_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/libxgboost-2.1.1-cpu_h7ae7b8f_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-64/libxml2-2.12.7-hc603aa4_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-64/libxml2-2.12.7-heaf3512_4.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/libzlib-1.3.1-h87427d6_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/lightgbm-4.5.0-cpu_py3.10h9c95325_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/llvm-openmp-18.1.8-h15ab845_0.conda
@@ -343,7 +339,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/brotli-bin-1.1.0-hb547adb_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/bzip2-1.0.8-h99b78c6_7.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/ca-certificates-2024.7.4-hf0a4a13_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/cairo-1.18.0-hc6c324b_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/cairo-1.18.0-hb4a6bf7_3.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/causalml-0.15.1-py310hd4a36ba_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2024.7.4-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cloudpickle-3.0.0-pyhd8ed1ab_0.conda
@@ -370,7 +366,6 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/fsspec-2024.6.1-pyhff2d567_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/future-1.0.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/gdk-pixbuf-2.42.12-h7ddc832_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/giflib-5.2.2-h93a5062_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/git_root-0.1-py_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/gmp-6.3.0-h7bae524_2.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/gmpy2-2.1.5-py310h3bc658a_1.conda
@@ -378,8 +373,8 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/graphviz-12.0.0-hbf8cc41_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/gtk2-2.24.33-h91d5085_5.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/gts-0.7.6-he42f4ea_4.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/harfbuzz-9.0.0-h1836168_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/icu-73.2-hc8870d7_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/harfbuzz-9.0.0-h997cde5_1.conda
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   license: MIT
   license_family: MIT
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   - graphite2
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   license_family: MIT
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   name: hpack
   version: 4.0.0
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   license_family: MIT
   size: 25341
   timestamp: 1598856368685
+- kind: conda
+  name: humanfriendly
+  version: '10.0'
+  build: py310h5588dad_5
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+  license: MIT
+  license_family: MIT
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+  timestamp: 1696713640192
+- kind: conda
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+  url: https://conda.anaconda.org/conda-forge/win-64/humanfriendly-10.0-py311h1ea47a8_5.conda
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+  license: MIT
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+  license: MIT
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+- kind: conda
+  name: humanfriendly
+  version: '10.0'
+  build: pyhd8ed1ab_6
+  build_number: 6
+  subdir: noarch
+  noarch: python
+  url: https://conda.anaconda.org/conda-forge/noarch/humanfriendly-10.0-pyhd8ed1ab_6.conda
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 - kind: conda
   name: hyperframe
   version: 6.0.1
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   license_family: MIT
   size: 12089150
   timestamp: 1692900650789
-- kind: conda
-  name: icu
-  version: '73.2'
-  build: h63175ca_0
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-  url: https://conda.anaconda.org/conda-forge/win-64/icu-73.2-h63175ca_0.conda
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-  timestamp: 1692901469029
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   name: icu
   version: '73.2'
@@ -11433,6 +12054,22 @@ packages:
   license_family: MIT
   size: 12129203
   timestamp: 1720853576813
+- kind: conda
+  name: icu
+  version: '75.1'
+  build: he0c23c2_0
+  subdir: win-64
+  url: https://conda.anaconda.org/conda-forge/win-64/icu-75.1-he0c23c2_0.conda
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+  md5: 8579b6bb8d18be7c0b27fb08adeeeb40
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+  - vc14_runtime >=14.29.30139
+  license: MIT
+  license_family: MIT
+  size: 14544252
+  timestamp: 1720853966338
 - kind: conda
   name: icu
   version: '75.1'
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 - kind: conda
   name: imagecodecs
   version: 2024.6.1
-  build: py310h1b8d656_2
-  build_number: 2
-  subdir: osx-64
-  url: https://conda.anaconda.org/conda-forge/osx-64/imagecodecs-2024.6.1-py310h1b8d656_2.conda
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-  md5: 490c8e51bfdf00b4751ebb29c666a3b8
+  build: py310h2c9cde1_3
+  build_number: 3
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/imagecodecs-2024.6.1-py310h2c9cde1_3.conda
+  sha256: e79f57eabb1425762e8afb3ba3925d0b819c0bbd15bf9ddb67789ebb07b0ce56
+  md5: 81a8b7321bb3df1e33abb8610a4b26f4
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   - brunsli >=0.1,<1.0a0
   - bzip2 >=1.0.8,<2.0a0
-  - c-blosc2 >=2.15.0,<2.16.0a0
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   - lerc >=4.0.0,<5.0a0
   - libaec >=1.1.3,<2.0a0
-  - libavif16 >=1.0.4,<2.0a0
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   - libbrotlidec >=1.1.0,<1.2.0a0
   - libbrotlienc >=1.1.0,<1.2.0a0
-  - libcxx >=16
-  - libdeflate >=1.20,<1.21.0a0
+  - libdeflate >=1.21,<1.22.0a0
+  - libgcc-ng >=12
   - libjpeg-turbo >=3.0.0,<4.0a0
   - libjxl >=0.10,<0.11.0a0
   - libpng >=1.6.43,<1.7.0a0
+  - libstdcxx-ng >=12
   - libtiff >=4.6.0,<4.7.0a0
   - libwebp-base >=1.4.0,<2.0a0
   - libzlib >=1.3.1,<2.0a0
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   - python >=3.10,<3.11.0a0
   - python_abi 3.10.* *_cp310
-  - snappy >=1.2.0,<1.3.0a0
+  - snappy >=1.2.1,<1.3.0a0
   - xz >=5.2.6,<6.0a0
   - zfp >=1.0.1,<2.0a0
   - zstd >=1.5.6,<1.6.0a0
   license: BSD-3-Clause
   license_family: BSD
-  size: 1650258
-  timestamp: 1719236653594
+  size: 2043972
+  timestamp: 1722900042831
 - kind: conda
   name: imagecodecs
   version: 2024.6.1
-  build: py310h331dd7d_2
-  build_number: 2
-  subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/imagecodecs-2024.6.1-py310h331dd7d_2.conda
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-  md5: 30a9bb62289aa1da94314208ebd989f8
+  build: py310h3ebe274_3
+  build_number: 3
+  subdir: win-64
+  url: https://conda.anaconda.org/conda-forge/win-64/imagecodecs-2024.6.1-py310h3ebe274_3.conda
+  sha256: 2a26b8087735d2c24b73f83959a72f09148a03ba5b0071aba63a8c85eb0ce7db
+  md5: b3e5f17829f6f5f6943b4aaff4c12f3f
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   - bzip2 >=1.0.8,<2.0a0
-  - c-blosc2 >=2.15.0,<2.16.0a0
+  - c-blosc2 >=2.15.1,<2.16.0a0
   - charls >=2.4.2,<2.5.0a0
   - giflib >=5.2.2,<5.3.0a0
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   - lerc >=4.0.0,<5.0a0
   - libaec >=1.1.3,<2.0a0
-  - libavif16 >=1.0.4,<2.0a0
+  - libavif16 >=1.1.1,<2.0a0
   - libbrotlicommon >=1.1.0,<1.2.0a0
   - libbrotlidec >=1.1.0,<1.2.0a0
   - libbrotlienc >=1.1.0,<1.2.0a0
-  - libcxx >=16
-  - libdeflate >=1.20,<1.21.0a0
+  - libdeflate >=1.21,<1.22.0a0
   - libjpeg-turbo >=3.0.0,<4.0a0
-  - libjxl >=0.10,<0.11.0a0
   - libpng >=1.6.43,<1.7.0a0
   - libtiff >=4.6.0,<4.7.0a0
   - libwebp-base >=1.4.0,<2.0a0
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   - openjpeg >=2.5.2,<3.0a0
   - python >=3.10,<3.11.0a0
-  - python >=3.10,<3.11.0a0 *_cpython
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-  - snappy >=1.2.0,<1.3.0a0
-  - xz >=5.2.6,<6.0a0
-  - zfp >=1.0.1,<2.0a0
+  - snappy >=1.2.1,<1.3.0a0
+  - ucrt >=10.0.20348.0
+  - vc >=14.2,<15
+  - vc14_runtime >=14.29.30139
+  - xz >=5.2.6,<6.0a0
+  - zfp >=1.0.1,<2.0a0
   - zstd >=1.5.6,<1.6.0a0
   license: BSD-3-Clause
   license_family: BSD
-  size: 1746734
-  timestamp: 1719236814820
+  size: 1583051
+  timestamp: 1722900340107
 - kind: conda
   name: imagecodecs
   version: 2024.6.1
-  build: py310h40cd1a8_2
-  build_number: 2
-  subdir: win-64
-  url: https://conda.anaconda.org/conda-forge/win-64/imagecodecs-2024.6.1-py310h40cd1a8_2.conda
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-  md5: b25b6a978244e40c1c73c9881df33a02
+  build: py310hdc0bfa8_3
+  build_number: 3
+  subdir: osx-64
+  url: https://conda.anaconda.org/conda-forge/osx-64/imagecodecs-2024.6.1-py310hdc0bfa8_3.conda
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+  md5: e87f3450ac84bcf1551cae9d24464cf6
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   - bzip2 >=1.0.8,<2.0a0
-  - c-blosc2 >=2.15.0,<2.16.0a0
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   - giflib >=5.2.2,<5.3.0a0
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   - lerc >=4.0.0,<5.0a0
   - libaec >=1.1.3,<2.0a0
-  - libavif16 >=1.0.4,<2.0a0
+  - libavif16 >=1.1.1,<2.0a0
   - libbrotlicommon >=1.1.0,<1.2.0a0
   - libbrotlidec >=1.1.0,<1.2.0a0
   - libbrotlienc >=1.1.0,<1.2.0a0
-  - libdeflate >=1.20,<1.21.0a0
+  - libcxx >=16
+  - libdeflate >=1.21,<1.22.0a0
   - libjpeg-turbo >=3.0.0,<4.0a0
+  - libjxl >=0.10,<0.11.0a0
   - libpng >=1.6.43,<1.7.0a0
   - libtiff >=4.6.0,<4.7.0a0
   - libwebp-base >=1.4.0,<2.0a0
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   - openjpeg >=2.5.2,<3.0a0
   - python >=3.10,<3.11.0a0
   - python_abi 3.10.* *_cp310
-  - snappy >=1.2.0,<1.3.0a0
-  - ucrt >=10.0.20348.0
-  - vc >=14.2,<15
-  - vc14_runtime >=14.29.30139
+  - snappy >=1.2.1,<1.3.0a0
   - xz >=5.2.6,<6.0a0
   - zfp >=1.0.1,<2.0a0
   - zstd >=1.5.6,<1.6.0a0
   license: BSD-3-Clause
   license_family: BSD
-  size: 1581531
-  timestamp: 1719237014473
+  size: 1647402
+  timestamp: 1722900136160
 - kind: conda
   name: imagecodecs
   version: 2024.6.1
-  build: py310h51fded0_2
-  build_number: 2
-  subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/imagecodecs-2024.6.1-py310h51fded0_2.conda
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-  md5: ac026fc13669395440a7ffa2c923e3fe
+  build: py310he13e3e9_3
+  build_number: 3
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/imagecodecs-2024.6.1-py310he13e3e9_3.conda
+  sha256: b228176edcc0b2266fed52b2f0a970b28fd8801659e275d4406a795aa72bb7fb
+  md5: 972d4807b0042eeab13a007b5df6d0f5
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   - bzip2 >=1.0.8,<2.0a0
-  - c-blosc2 >=2.15.0,<2.16.0a0
+  - c-blosc2 >=2.15.1,<2.16.0a0
   - charls >=2.4.2,<2.5.0a0
   - giflib >=5.2.2,<5.3.0a0
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   - lerc >=4.0.0,<5.0a0
   - libaec >=1.1.3,<2.0a0
-  - libavif16 >=1.0.4,<2.0a0
+  - libavif16 >=1.1.1,<2.0a0
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   - libbrotlidec >=1.1.0,<1.2.0a0
   - libbrotlienc >=1.1.0,<1.2.0a0
-  - libdeflate >=1.20,<1.21.0a0
-  - libgcc-ng >=12
+  - libcxx >=16
+  - libdeflate >=1.21,<1.22.0a0
   - libjpeg-turbo >=3.0.0,<4.0a0
   - libjxl >=0.10,<0.11.0a0
   - libpng >=1.6.43,<1.7.0a0
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@@ -11657,44 +12294,41 @@ packages:
   - numpy >=1.19,<3
   - openjpeg >=2.5.2,<3.0a0
   - python >=3.10,<3.11.0a0
+  - python >=3.10,<3.11.0a0 *_cpython
   - python_abi 3.10.* *_cp310
-  - snappy >=1.2.0,<1.3.0a0
+  - snappy >=1.2.1,<1.3.0a0
   - xz >=5.2.6,<6.0a0
   - zfp >=1.0.1,<2.0a0
   - zstd >=1.5.6,<1.6.0a0
   license: BSD-3-Clause
   license_family: BSD
-  size: 2050147
-  timestamp: 1719236718925
+  size: 1743627
+  timestamp: 1722899981228
 - kind: conda
   name: imagecodecs
   version: 2024.6.1
-  build: py311h0fab4c6_2
-  build_number: 2
-  subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/imagecodecs-2024.6.1-py311h0fab4c6_2.conda
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-  md5: 3da586a35728a2afb88ae9ba62e81283
+  build: py311h509772e_3
+  build_number: 3
+  subdir: win-64
+  url: https://conda.anaconda.org/conda-forge/win-64/imagecodecs-2024.6.1-py311h509772e_3.conda
+  sha256: 35ca9caa198358b2f6768c0d3d5d74e0465c2bb4c32168d292d5ca74bf0af930
+  md5: a33f223b1e4bab1b4ee3b5351bcefba8
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-  - c-blosc2 >=2.15.0,<2.16.0a0
+  - c-blosc2 >=2.15.1,<2.16.0a0
   - charls >=2.4.2,<2.5.0a0
   - giflib >=5.2.2,<5.3.0a0
   - jxrlib >=1.1,<1.2.0a0
   - lcms2 >=2.16,<3.0a0
   - lerc >=4.0.0,<5.0a0
   - libaec >=1.1.3,<2.0a0
-  - libavif16 >=1.0.4,<2.0a0
+  - libavif16 >=1.1.1,<2.0a0
   - libbrotlicommon >=1.1.0,<1.2.0a0
   - libbrotlidec >=1.1.0,<1.2.0a0
   - libbrotlienc >=1.1.0,<1.2.0a0
-  - libcxx >=16
-  - libdeflate >=1.20,<1.21.0a0
+  - libdeflate >=1.21,<1.22.0a0
   - libjpeg-turbo >=3.0.0,<4.0a0
-  - libjxl >=0.10,<0.11.0a0
   - libpng >=1.6.43,<1.7.0a0
   - libtiff >=4.6.0,<4.7.0a0
   - libwebp-base >=1.4.0,<2.0a0
@@ -11704,43 +12338,45 @@ packages:
   - numpy >=1.19,<3
   - openjpeg >=2.5.2,<3.0a0
   - python >=3.11,<3.12.0a0
-  - python >=3.11,<3.12.0a0 *_cpython
   - python_abi 3.11.* *_cp311
-  - snappy >=1.2.0,<1.3.0a0
+  - snappy >=1.2.1,<1.3.0a0
+  - ucrt >=10.0.20348.0
+  - vc >=14.2,<15
+  - vc14_runtime >=14.29.30139
   - xz >=5.2.6,<6.0a0
   - zfp >=1.0.1,<2.0a0
   - zstd >=1.5.6,<1.6.0a0
   license: BSD-3-Clause
   license_family: BSD
-  size: 1772323
-  timestamp: 1719237069581
+  size: 1609562
+  timestamp: 1722900215383
 - kind: conda
   name: imagecodecs
   version: 2024.6.1
-  build: py311h8ebd238_2
-  build_number: 2
+  build: py311h5db3a85_3
+  build_number: 3
   subdir: osx-64
-  url: https://conda.anaconda.org/conda-forge/osx-64/imagecodecs-2024.6.1-py311h8ebd238_2.conda
-  sha256: f1666ba967a7c4e796d049e925a6962c1e8403ff8e277ab19d25dd78a5be4720
-  md5: 7ae7d17cf2cfe98f21d552103effb1f6
+  url: https://conda.anaconda.org/conda-forge/osx-64/imagecodecs-2024.6.1-py311h5db3a85_3.conda
+  sha256: a67ff3bab6cd30f958127599151af0b60230fd7481a0f6f17172047b5c56eccf
+  md5: 5f2febdd293a0797e0525dadb39421b4
   depends:
   - __osx >=10.13
-  - blosc >=1.21.5,<2.0a0
+  - blosc >=1.21.6,<2.0a0
   - brunsli >=0.1,<1.0a0
   - bzip2 >=1.0.8,<2.0a0
-  - c-blosc2 >=2.15.0,<2.16.0a0
+  - c-blosc2 >=2.15.1,<2.16.0a0
   - charls >=2.4.2,<2.5.0a0
   - giflib >=5.2.2,<5.3.0a0
   - jxrlib >=1.1,<1.2.0a0
   - lcms2 >=2.16,<3.0a0
   - lerc >=4.0.0,<5.0a0
   - libaec >=1.1.3,<2.0a0
-  - libavif16 >=1.0.4,<2.0a0
+  - libavif16 >=1.1.1,<2.0a0
   - libbrotlicommon >=1.1.0,<1.2.0a0
   - libbrotlidec >=1.1.0,<1.2.0a0
   - libbrotlienc >=1.1.0,<1.2.0a0
   - libcxx >=16
-  - libdeflate >=1.20,<1.21.0a0
+  - libdeflate >=1.21,<1.22.0a0
   - libjpeg-turbo >=3.0.0,<4.0a0
   - libjxl >=0.10,<0.11.0a0
   - libpng >=1.6.43,<1.7.0a0
@@ -11753,39 +12389,40 @@ packages:
   - openjpeg >=2.5.2,<3.0a0
   - python >=3.11,<3.12.0a0
   - python_abi 3.11.* *_cp311
-  - snappy >=1.2.0,<1.3.0a0
+  - snappy >=1.2.1,<1.3.0a0
   - xz >=5.2.6,<6.0a0
   - zfp >=1.0.1,<2.0a0
   - zstd >=1.5.6,<1.6.0a0
   license: BSD-3-Clause
   license_family: BSD
-  size: 1676438
-  timestamp: 1719236909244
+  size: 1684473
+  timestamp: 1722900074005
 - kind: conda
   name: imagecodecs
   version: 2024.6.1
-  build: py311hb8791aa_2
-  build_number: 2
+  build: py311h732c098_3
+  build_number: 3
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/imagecodecs-2024.6.1-py311hb8791aa_2.conda
-  sha256: e3f598e8b17868c12a2f49e9d2163e9732e96e99cca675e0ab1fbbee826ebf43
-  md5: 32be55fe61a4656fb476556b7acdb384
+  url: https://conda.anaconda.org/conda-forge/linux-64/imagecodecs-2024.6.1-py311h732c098_3.conda
+  sha256: e64844785a22e007c7e8dce66517c7bc80a1df3b5df80e9e8d5c83da1bbafb11
+  md5: 34ee94453a09c0abc94b194207917b50
   depends:
-  - blosc >=1.21.5,<2.0a0
+  - __glibc >=2.17,<3.0.a0
+  - blosc >=1.21.6,<2.0a0
   - brunsli >=0.1,<1.0a0
   - bzip2 >=1.0.8,<2.0a0
-  - c-blosc2 >=2.15.0,<2.16.0a0
+  - c-blosc2 >=2.15.1,<2.16.0a0
   - charls >=2.4.2,<2.5.0a0
   - giflib >=5.2.2,<5.3.0a0
   - jxrlib >=1.1,<1.2.0a0
   - lcms2 >=2.16,<3.0a0
   - lerc >=4.0.0,<5.0a0
   - libaec >=1.1.3,<2.0a0
-  - libavif16 >=1.0.4,<2.0a0
+  - libavif16 >=1.1.1,<2.0a0
   - libbrotlicommon >=1.1.0,<1.2.0a0
   - libbrotlidec >=1.1.0,<1.2.0a0
   - libbrotlienc >=1.1.0,<1.2.0a0
-  - libdeflate >=1.20,<1.21.0a0
+  - libdeflate >=1.21,<1.22.0a0
   - libgcc-ng >=12
   - libjpeg-turbo >=3.0.0,<4.0a0
   - libjxl >=0.10,<0.11.0a0
@@ -11800,39 +12437,43 @@ packages:
   - openjpeg >=2.5.2,<3.0a0
   - python >=3.11,<3.12.0a0
   - python_abi 3.11.* *_cp311
-  - snappy >=1.2.0,<1.3.0a0
+  - snappy >=1.2.1,<1.3.0a0
   - xz >=5.2.6,<6.0a0
   - zfp >=1.0.1,<2.0a0
   - zstd >=1.5.6,<1.6.0a0
   license: BSD-3-Clause
   license_family: BSD
-  size: 2056781
-  timestamp: 1719236700395
+  size: 2054419
+  timestamp: 1722900038733
 - kind: conda
   name: imagecodecs
   version: 2024.6.1
-  build: py311hd929db6_2
-  build_number: 2
-  subdir: win-64
-  url: https://conda.anaconda.org/conda-forge/win-64/imagecodecs-2024.6.1-py311hd929db6_2.conda
-  sha256: ef3ad88549d442c9a4894a75fc70547eb083bc2330b51d220791d1c89e10429a
-  md5: 85cbd41aafac750a3c4d0c6fbfd3cbbc
+  build: py311h9541020_3
+  build_number: 3
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/imagecodecs-2024.6.1-py311h9541020_3.conda
+  sha256: 690d324b87e813cf5a4306aeb41c347414c0038b21e76c13ae35f58be0c7c375
+  md5: 2965ba7c6dcadb4323681e6e63d685bf
   depends:
-  - blosc >=1.21.5,<2.0a0
+  - __osx >=11.0
+  - blosc >=1.21.6,<2.0a0
+  - brunsli >=0.1,<1.0a0
   - bzip2 >=1.0.8,<2.0a0
-  - c-blosc2 >=2.15.0,<2.16.0a0
+  - c-blosc2 >=2.15.1,<2.16.0a0
   - charls >=2.4.2,<2.5.0a0
   - giflib >=5.2.2,<5.3.0a0
   - jxrlib >=1.1,<1.2.0a0
   - lcms2 >=2.16,<3.0a0
   - lerc >=4.0.0,<5.0a0
   - libaec >=1.1.3,<2.0a0
-  - libavif16 >=1.0.4,<2.0a0
+  - libavif16 >=1.1.1,<2.0a0
   - libbrotlicommon >=1.1.0,<1.2.0a0
   - libbrotlidec >=1.1.0,<1.2.0a0
   - libbrotlienc >=1.1.0,<1.2.0a0
-  - libdeflate >=1.20,<1.21.0a0
+  - libcxx >=16
+  - libdeflate >=1.21,<1.22.0a0
   - libjpeg-turbo >=3.0.0,<4.0a0
+  - libjxl >=0.10,<0.11.0a0
   - libpng >=1.6.43,<1.7.0a0
   - libtiff >=4.6.0,<4.7.0a0
   - libwebp-base >=1.4.0,<2.0a0
@@ -11842,48 +12483,47 @@ packages:
   - numpy >=1.19,<3
   - openjpeg >=2.5.2,<3.0a0
   - python >=3.11,<3.12.0a0
+  - python >=3.11,<3.12.0a0 *_cpython
   - python_abi 3.11.* *_cp311
-  - snappy >=1.2.0,<1.3.0a0
-  - ucrt >=10.0.20348.0
-  - vc >=14.2,<15
-  - vc14_runtime >=14.29.30139
+  - snappy >=1.2.1,<1.3.0a0
   - xz >=5.2.6,<6.0a0
   - zfp >=1.0.1,<2.0a0
   - zstd >=1.5.6,<1.6.0a0
   license: BSD-3-Clause
   license_family: BSD
-  size: 1609531
-  timestamp: 1719237001536
+  size: 1770725
+  timestamp: 1722899976293
 - kind: conda
   name: imagecodecs
   version: 2024.6.1
-  build: py312h5755a8a_2
-  build_number: 2
-  subdir: osx-64
-  url: https://conda.anaconda.org/conda-forge/osx-64/imagecodecs-2024.6.1-py312h5755a8a_2.conda
-  sha256: 8d3a3cc94cff910b705ae9d0290f5d137f08d5c3f7df8d95e7e9f5386620cb90
-  md5: 1935447d7fa18910e95ce3e96a8b53e9
+  build: py312h36edd9b_3
+  build_number: 3
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/imagecodecs-2024.6.1-py312h36edd9b_3.conda
+  sha256: b8cb061810628487fb02f9f8d7cb110e94b99cf6dd6bb14b85c5cb601770d62d
+  md5: c309596a0ff59342f52e59046bbe3a8c
   depends:
-  - __osx >=10.13
-  - blosc >=1.21.5,<2.0a0
+  - __glibc >=2.17,<3.0.a0
+  - blosc >=1.21.6,<2.0a0
   - brunsli >=0.1,<1.0a0
   - bzip2 >=1.0.8,<2.0a0
-  - c-blosc2 >=2.15.0,<2.16.0a0
+  - c-blosc2 >=2.15.1,<2.16.0a0
   - charls >=2.4.2,<2.5.0a0
   - giflib >=5.2.2,<5.3.0a0
   - jxrlib >=1.1,<1.2.0a0
   - lcms2 >=2.16,<3.0a0
   - lerc >=4.0.0,<5.0a0
   - libaec >=1.1.3,<2.0a0
-  - libavif16 >=1.0.4,<2.0a0
+  - libavif16 >=1.1.1,<2.0a0
   - libbrotlicommon >=1.1.0,<1.2.0a0
   - libbrotlidec >=1.1.0,<1.2.0a0
   - libbrotlienc >=1.1.0,<1.2.0a0
-  - libcxx >=16
-  - libdeflate >=1.20,<1.21.0a0
+  - libdeflate >=1.21,<1.22.0a0
+  - libgcc-ng >=12
   - libjpeg-turbo >=3.0.0,<4.0a0
   - libjxl >=0.10,<0.11.0a0
   - libpng >=1.6.43,<1.7.0a0
+  - libstdcxx-ng >=12
   - libtiff >=4.6.0,<4.7.0a0
   - libwebp-base >=1.4.0,<2.0a0
   - libzlib >=1.3.1,<2.0a0
@@ -11893,44 +12533,44 @@ packages:
   - openjpeg >=2.5.2,<3.0a0
   - python >=3.12,<3.13.0a0
   - python_abi 3.12.* *_cp312
-  - snappy >=1.2.0,<1.3.0a0
+  - snappy >=1.2.1,<1.3.0a0
   - xz >=5.2.6,<6.0a0
   - zfp >=1.0.1,<2.0a0
   - zstd >=1.5.6,<1.6.0a0
   license: BSD-3-Clause
   license_family: BSD
-  size: 1656676
-  timestamp: 1719236780404
+  size: 2073034
+  timestamp: 1722899938174
 - kind: conda
   name: imagecodecs
   version: 2024.6.1
-  build: py312h6b2d42b_2
-  build_number: 2
-  subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/imagecodecs-2024.6.1-py312h6b2d42b_2.conda
-  sha256: 494058c2ab1ede38a22a31ff278ba899eb862e128f3344ea94e0b7cff6818ed1
-  md5: f71c56fe03000e3e2c4e7fa72069eac4
+  build: py312h8902707_3
+  build_number: 3
+  subdir: osx-64
+  url: https://conda.anaconda.org/conda-forge/osx-64/imagecodecs-2024.6.1-py312h8902707_3.conda
+  sha256: 463e775d9eedeb272b31bdf6e300acf2c91a80e3e796ce47fde2e961b421ed66
+  md5: a33ab62781c28ab70e53ed38fc4702e7
   depends:
-  - blosc >=1.21.5,<2.0a0
+  - __osx >=10.13
+  - blosc >=1.21.6,<2.0a0
   - brunsli >=0.1,<1.0a0
   - bzip2 >=1.0.8,<2.0a0
-  - c-blosc2 >=2.15.0,<2.16.0a0
+  - c-blosc2 >=2.15.1,<2.16.0a0
   - charls >=2.4.2,<2.5.0a0
   - giflib >=5.2.2,<5.3.0a0
   - jxrlib >=1.1,<1.2.0a0
   - lcms2 >=2.16,<3.0a0
   - lerc >=4.0.0,<5.0a0
   - libaec >=1.1.3,<2.0a0
-  - libavif16 >=1.0.4,<2.0a0
+  - libavif16 >=1.1.1,<2.0a0
   - libbrotlicommon >=1.1.0,<1.2.0a0
   - libbrotlidec >=1.1.0,<1.2.0a0
   - libbrotlienc >=1.1.0,<1.2.0a0
-  - libdeflate >=1.20,<1.21.0a0
-  - libgcc-ng >=12
+  - libcxx >=16
+  - libdeflate >=1.21,<1.22.0a0
   - libjpeg-turbo >=3.0.0,<4.0a0
   - libjxl >=0.10,<0.11.0a0
   - libpng >=1.6.43,<1.7.0a0
-  - libstdcxx-ng >=12
   - libtiff >=4.6.0,<4.7.0a0
   - libwebp-base >=1.4.0,<2.0a0
   - libzlib >=1.3.1,<2.0a0
@@ -11940,43 +12580,39 @@ packages:
   - openjpeg >=2.5.2,<3.0a0
   - python >=3.12,<3.13.0a0
   - python_abi 3.12.* *_cp312
-  - snappy >=1.2.0,<1.3.0a0
+  - snappy >=1.2.1,<1.3.0a0
   - xz >=5.2.6,<6.0a0
   - zfp >=1.0.1,<2.0a0
   - zstd >=1.5.6,<1.6.0a0
   license: BSD-3-Clause
   license_family: BSD
-  size: 2069273
-  timestamp: 1719236704595
+  size: 1656876
+  timestamp: 1722900095184
 - kind: conda
   name: imagecodecs
   version: 2024.6.1
-  build: py312h7304456_2
-  build_number: 2
-  subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/imagecodecs-2024.6.1-py312h7304456_2.conda
-  sha256: d1d1725c3286b3722b9c633bf2e4fa8daf74ca633f725e9f6505c948572ecb63
-  md5: 1b74007f7eea2706b527c6574d2ddb5b
+  build: py312hd2fe4a3_3
+  build_number: 3
+  subdir: win-64
+  url: https://conda.anaconda.org/conda-forge/win-64/imagecodecs-2024.6.1-py312hd2fe4a3_3.conda
+  sha256: cfb62c7846f2a464cfcbcceb167897a0d0fb10700220b6f256a49d9ef382019a
+  md5: 67c2d384e46c46d64d4fd6e708071479
   depends:
-  - __osx >=11.0
-  - blosc >=1.21.5,<2.0a0
-  - brunsli >=0.1,<1.0a0
+  - blosc >=1.21.6,<2.0a0
   - bzip2 >=1.0.8,<2.0a0
-  - c-blosc2 >=2.15.0,<2.16.0a0
+  - c-blosc2 >=2.15.1,<2.16.0a0
   - charls >=2.4.2,<2.5.0a0
   - giflib >=5.2.2,<5.3.0a0
   - jxrlib >=1.1,<1.2.0a0
   - lcms2 >=2.16,<3.0a0
   - lerc >=4.0.0,<5.0a0
   - libaec >=1.1.3,<2.0a0
-  - libavif16 >=1.0.4,<2.0a0
+  - libavif16 >=1.1.1,<2.0a0
   - libbrotlicommon >=1.1.0,<1.2.0a0
   - libbrotlidec >=1.1.0,<1.2.0a0
   - libbrotlienc >=1.1.0,<1.2.0a0
-  - libcxx >=16
-  - libdeflate >=1.20,<1.21.0a0
+  - libdeflate >=1.21,<1.22.0a0
   - libjpeg-turbo >=3.0.0,<4.0a0
-  - libjxl >=0.10,<0.11.0a0
   - libpng >=1.6.43,<1.7.0a0
   - libtiff >=4.6.0,<4.7.0a0
   - libwebp-base >=1.4.0,<2.0a0
@@ -11986,41 +12622,47 @@ packages:
   - numpy >=1.19,<3
   - openjpeg >=2.5.2,<3.0a0
   - python >=3.12,<3.13.0a0
-  - python >=3.12,<3.13.0a0 *_cpython
   - python_abi 3.12.* *_cp312
-  - snappy >=1.2.0,<1.3.0a0
+  - snappy >=1.2.1,<1.3.0a0
+  - ucrt >=10.0.20348.0
+  - vc >=14.2,<15
+  - vc14_runtime >=14.29.30139
   - xz >=5.2.6,<6.0a0
   - zfp >=1.0.1,<2.0a0
   - zstd >=1.5.6,<1.6.0a0
   license: BSD-3-Clause
   license_family: BSD
-  size: 1746996
-  timestamp: 1719236588237
+  size: 1586933
+  timestamp: 1722900214784
 - kind: conda
   name: imagecodecs
   version: 2024.6.1
-  build: py312hecf518a_2
-  build_number: 2
-  subdir: win-64
-  url: https://conda.anaconda.org/conda-forge/win-64/imagecodecs-2024.6.1-py312hecf518a_2.conda
-  sha256: 4f20269a586c225b998eed6336e85a23726e7b6d479a464556dc0fc644637171
-  md5: 267b973926fb052ec57f03921c2d885a
+  build: py312hd96b8b9_3
+  build_number: 3
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/imagecodecs-2024.6.1-py312hd96b8b9_3.conda
+  sha256: 7392023ddba79679a40957992cd4e10c8930318574bfcdcb5f94d18b464cd5cf
+  md5: 35983d5cf3191c036d30727aaf475963
   depends:
-  - blosc >=1.21.5,<2.0a0
+  - __osx >=11.0
+  - blosc >=1.21.6,<2.0a0
+  - brunsli >=0.1,<1.0a0
   - bzip2 >=1.0.8,<2.0a0
-  - c-blosc2 >=2.15.0,<2.16.0a0
+  - c-blosc2 >=2.15.1,<2.16.0a0
   - charls >=2.4.2,<2.5.0a0
   - giflib >=5.2.2,<5.3.0a0
   - jxrlib >=1.1,<1.2.0a0
   - lcms2 >=2.16,<3.0a0
   - lerc >=4.0.0,<5.0a0
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   timestamp: 1720857377573
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   build: py311he736701_0
   subdir: win-64
-  url: https://conda.anaconda.org/conda-forge/win-64/sqlalchemy-2.0.31-py311he736701_0.conda
-  sha256: 6aea793ce27eef0161132e2b584d5e7bc39c4c814966eb7c721abca6e6810d10
-  md5: e78e362c943becacb121d691f991c8f6
+  url: https://conda.anaconda.org/conda-forge/win-64/sqlalchemy-2.0.32-py311he736701_0.conda
+  sha256: 2c299eebd48a969927542ea7bc475bb8607f8440ef0692c17c30e59614eb1b02
+  md5: 7f48222288e535a5ea2ad6d355fa1b4a
   depends:
   - greenlet !=0.4.17
   - python >=3.11,<3.12.0a0
@@ -26125,17 +27396,34 @@ packages:
   - vc >=14.2,<15
   - vc14_runtime >=14.29.30139
   license: MIT
-  license_family: MIT
-  size: 3475380
-  timestamp: 1718781632682
+  size: 3465892
+  timestamp: 1722927619965
+- kind: conda
+  name: sqlalchemy
+  version: 2.0.32
+  build: py312h41a817b_0
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/sqlalchemy-2.0.32-py312h41a817b_0.conda
+  sha256: e75d677e37d395f934e87b887a420b1a7b4b5a9a5fd0e65846901f4cfd68a314
+  md5: 3ac8df9507553117fd7b9daf79ff1269
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - greenlet !=0.4.17
+  - libgcc-ng >=12
+  - python >=3.12,<3.13.0a0
+  - python_abi 3.12.* *_cp312
+  - typing-extensions >=4.6.0
+  license: MIT
+  size: 3484882
+  timestamp: 1722927210468
 - kind: conda
   name: sqlalchemy
-  version: 2.0.31
+  version: 2.0.32
   build: py312h4389bb4_0
   subdir: win-64
-  url: https://conda.anaconda.org/conda-forge/win-64/sqlalchemy-2.0.31-py312h4389bb4_0.conda
-  sha256: c540d1e5956cd5c0aca6fc9c46e9648e53f8e4c9bd8f96acdf64c0cbacdb0697
-  md5: f8cbd51d7d35c98987ae10892e3e8bdb
+  url: https://conda.anaconda.org/conda-forge/win-64/sqlalchemy-2.0.32-py312h4389bb4_0.conda
+  sha256: bd3e88f5244a3a64fd8c18cd140c62e42706ef3523d35e8a412ec84a7b19a1e6
+  md5: e990eba5d45ebdd6676f36180010733f
   depends:
   - greenlet !=0.4.17
   - python >=3.12,<3.13.0a0
@@ -26145,17 +27433,16 @@ packages:
   - vc >=14.2,<15
   - vc14_runtime >=14.29.30139
   license: MIT
-  license_family: MIT
-  size: 3413861
-  timestamp: 1718781722471
+  size: 3441258
+  timestamp: 1722927832901
 - kind: conda
   name: sqlalchemy
-  version: 2.0.31
+  version: 2.0.32
   build: py312h7e5086c_0
   subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/sqlalchemy-2.0.31-py312h7e5086c_0.conda
-  sha256: 519d3da40f13f8b7f602ecd8f2f85e9d2271f4f57c95276011a31318d37a4e44
-  md5: acaa288b3fe2f9ab799c8b9ec6e32979
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/sqlalchemy-2.0.32-py312h7e5086c_0.conda
+  sha256: 6f69acb02523bf02ca00d6dbf6d688ed3c3e6b9aa7511ab6c1680fa415796eb5
+  md5: efdf833555d35528ab61de749b7ab429
   depends:
   - __osx >=11.0
   - greenlet !=0.4.17
@@ -26164,35 +27451,16 @@ packages:
   - python_abi 3.12.* *_cp312
   - typing-extensions >=4.6.0
   license: MIT
-  license_family: MIT
-  size: 3459336
-  timestamp: 1718781480950
-- kind: conda
-  name: sqlalchemy
-  version: 2.0.31
-  build: py312h9a8786e_0
-  subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/sqlalchemy-2.0.31-py312h9a8786e_0.conda
-  sha256: a372cd1c3d153bb0620894e30b07bd54fb81ba4ad34ffd64a99d76a55d2eb8f1
-  md5: 81b5a42f9aca877913472eeae0018165
-  depends:
-  - greenlet !=0.4.17
-  - libgcc-ng >=12
-  - python >=3.12,<3.13.0a0
-  - python_abi 3.12.* *_cp312
-  - typing-extensions >=4.6.0
-  license: MIT
-  license_family: MIT
-  size: 3494447
-  timestamp: 1718781304578
+  size: 3485812
+  timestamp: 1722927321083
 - kind: conda
   name: sqlalchemy
-  version: 2.0.31
+  version: 2.0.32
   build: py312hbd25219_0
   subdir: osx-64
-  url: https://conda.anaconda.org/conda-forge/osx-64/sqlalchemy-2.0.31-py312hbd25219_0.conda
-  sha256: 61ec515da408c009eda5d17869161df757dc0aef0cacd3b68749d04c54d9644c
-  md5: fc27c3c584ce634eb9358ab7e55d39db
+  url: https://conda.anaconda.org/conda-forge/osx-64/sqlalchemy-2.0.32-py312hbd25219_0.conda
+  sha256: d79f8ab2105f3a70967d75e8e43bba30cc7bac46ca0f411e25506c584c9a5368
+  md5: 041c8516eaddd2960f7f0730032f212a
   depends:
   - __osx >=10.13
   - greenlet !=0.4.17
@@ -26200,9 +27468,8 @@ packages:
   - python_abi 3.12.* *_cp312
   - typing-extensions >=4.6.0
   license: MIT
-  license_family: MIT
-  size: 3442561
-  timestamp: 1718781374126
+  size: 3449840
+  timestamp: 1722927250841
 - kind: conda
   name: sqlite
   version: 3.46.0
@@ -27306,18 +28573,18 @@ packages:
   timestamp: 1713535244513
 - kind: conda
   name: types-setuptools
-  version: 71.1.0.20240726
+  version: 71.1.0.20240806
   build: pyhd8ed1ab_0
   subdir: noarch
   noarch: python
-  url: https://conda.anaconda.org/conda-forge/noarch/types-setuptools-71.1.0.20240726-pyhd8ed1ab_0.conda
-  sha256: ec4c28121a781334115b64d955e178419f3f4a58ee1361942b27aae3d3934d38
-  md5: 644f050501361142a5d9b0efc28967a5
+  url: https://conda.anaconda.org/conda-forge/noarch/types-setuptools-71.1.0.20240806-pyhd8ed1ab_0.conda
+  sha256: 9b7993b78fb851ff386f0534b8d1211ad5591f943fbab609bb66b88fa3c85a75
+  md5: 4da5aaf5fd74083f1a6ef6634b921c12
   depends:
   - python >=3.8
   license: Apache-2.0 AND MIT
-  size: 46989
-  timestamp: 1721973461093
+  size: 46936
+  timestamp: 1722930195524
 - kind: conda
   name: typing-extensions
   version: 4.12.2
diff --git a/pixi.toml b/pixi.toml
index e4c49326..ff5a9750 100644
--- a/pixi.toml
+++ b/pixi.toml
@@ -34,6 +34,10 @@ shap = "*"
 matplotlib-base = "*"
 joblib = ">=1.2.0"
 lifelines = "*"
+onnx = "*"
+onnxmltools = "*"
+onnxruntime = "*"
+spox = "*"
 
 [feature.lint.dependencies]
 pre-commit = "*"
diff --git a/tests/conftest.py b/tests/conftest.py
index 9ef37d26..93ffe213 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -7,6 +7,65 @@
 import pandas as pd
 import pytest
 from git_root import git_root
+from sklearn.calibration import CalibratedClassifierCV
+from sklearn.discriminant_analysis import (
+    QuadraticDiscriminantAnalysis,
+)
+from sklearn.ensemble import (
+    AdaBoostRegressor,
+    BaggingClassifier,
+    BaggingRegressor,
+    ExtraTreesClassifier,
+    ExtraTreesRegressor,
+    GradientBoostingClassifier,
+    GradientBoostingRegressor,
+    HistGradientBoostingClassifier,
+    HistGradientBoostingRegressor,
+    RandomForestClassifier,
+    RandomForestRegressor,
+)
+from sklearn.gaussian_process import GaussianProcessRegressor
+from sklearn.linear_model import (
+    ARDRegression,
+    BayesianRidge,
+    ElasticNet,
+    ElasticNetCV,
+    HuberRegressor,
+    Lars,
+    LarsCV,
+    Lasso,
+    LassoCV,
+    LassoLars,
+    LassoLarsCV,
+    LassoLarsIC,
+    LinearRegression,
+    LogisticRegression,
+    LogisticRegressionCV,
+    OrthogonalMatchingPursuit,
+    OrthogonalMatchingPursuitCV,
+    PassiveAggressiveRegressor,
+    QuantileRegressor,
+    RANSACRegressor,
+    Ridge,
+    RidgeCV,
+    SGDRegressor,
+    TheilSenRegressor,
+    TweedieRegressor,
+)
+from sklearn.neighbors import (
+    KNeighborsClassifier,
+    KNeighborsRegressor,
+    RadiusNeighborsClassifier,
+    RadiusNeighborsRegressor,
+)
+from sklearn.neural_network import MLPClassifier, MLPRegressor
+from sklearn.svm import SVR, LinearSVR, NuSVR
+from sklearn.tree import (
+    DecisionTreeClassifier,
+    DecisionTreeRegressor,
+    ExtraTreeClassifier,
+    ExtraTreeRegressor,
+)
 
 from metalearners._utils import get_linear_dimension, load_mindset_data, load_twins_data
 from metalearners.data_generation import (
@@ -22,6 +81,68 @@
 _SEED = 1337
 _SIGMA_TAU = 0.5
 
+all_sklearn_classifiers = [
+    # AdaBoostClassifier, # The output probabilities are wrong when there are only two classes, see https://github.com/onnx/sklearn-onnx/issues/1117
+    BaggingClassifier,
+    CalibratedClassifierCV,
+    DecisionTreeClassifier,
+    ExtraTreeClassifier,
+    ExtraTreesClassifier,
+    # GaussianProcessClassifier, # This raises an error com.microsoft:Solve(-1) is not a registered function/op when inference on onnx. TODO: investigate it further
+    GradientBoostingClassifier,
+    HistGradientBoostingClassifier,
+    KNeighborsClassifier,
+    # LinearDiscriminantAnalysis, # The output probabilities are wrong when there are only two classes, see https://github.com/onnx/sklearn-onnx/issues/1116
+    LogisticRegression,
+    LogisticRegressionCV,
+    MLPClassifier,
+    QuadraticDiscriminantAnalysis,
+    RadiusNeighborsClassifier,
+    RandomForestClassifier,
+]  # extracted from all_estimators("classifier"), models which have predict_proba and convert_sklearn supports them
+
+all_sklearn_regressors = [
+    ARDRegression,
+    AdaBoostRegressor,
+    BaggingRegressor,
+    BayesianRidge,
+    DecisionTreeRegressor,
+    ElasticNet,
+    ElasticNetCV,
+    ExtraTreeRegressor,
+    ExtraTreesRegressor,
+    GaussianProcessRegressor,
+    GradientBoostingRegressor,
+    HistGradientBoostingRegressor,
+    HuberRegressor,
+    KNeighborsRegressor,
+    Lars,
+    LarsCV,
+    Lasso,
+    LassoCV,
+    LassoLars,
+    LassoLarsCV,
+    LassoLarsIC,
+    LinearRegression,
+    LinearSVR,
+    MLPRegressor,
+    NuSVR,
+    OrthogonalMatchingPursuit,
+    OrthogonalMatchingPursuitCV,
+    # PLSRegression, # The output shape of the onnx converted model is wrong
+    PassiveAggressiveRegressor,
+    QuantileRegressor,
+    RANSACRegressor,
+    RadiusNeighborsRegressor,
+    RandomForestRegressor,
+    Ridge,
+    RidgeCV,
+    SGDRegressor,
+    SVR,
+    TheilSenRegressor,
+    TweedieRegressor,
+]  # regressors which are supported by convert_sklearn and support regression in the reals
+
 
 def _generate_rct_experiment_data(
     covariates,
@@ -246,3 +367,27 @@ def grid_search_data():
     w_test = rng.integers(0, n_variants, n_test_samples)
 
     return X, y_class, y_reg, w, X_test, y_test_class, y_test_reg, w_test
+
+
+@pytest.fixture(scope="session")
+def onnx_dataset():
+    rng = np.random.default_rng(_SEED)
+    n_samples = 300
+    n_numerical_features = 5
+
+    X_numerical = rng.standard_normal((n_samples, n_numerical_features))
+
+    X_with_categorical = pd.DataFrame(X_numerical)
+    X_with_categorical[n_numerical_features] = pd.Series(
+        rng.integers(10, 13, n_samples), dtype="category"
+    )  # not start at 0
+    X_with_categorical[n_numerical_features + 1] = pd.Series(
+        rng.choice([-5, 4, -10, -32], size=n_samples), dtype="category"
+    )  # not consecutive
+
+    y_class = rng.integers(0, 2, size=n_samples)
+    y_reg = rng.standard_normal(n_samples)
+
+    w = rng.integers(0, 3, n_samples)
+
+    return X_numerical, X_with_categorical, y_class, y_reg, w
diff --git a/tests/test_drlearner.py b/tests/test_drlearner.py
index 1e8d3c5d..14298e21 100644
--- a/tests/test_drlearner.py
+++ b/tests/test_drlearner.py
@@ -1,10 +1,24 @@
 # Copyright (c) QuantCo 2024-2024
 # SPDX-License-Identifier: BSD-3-Clause
 
+from itertools import repeat
+
 import numpy as np
+import onnxruntime as rt
+import pytest
+from lightgbm import LGBMClassifier, LGBMRegressor
+from onnx import ModelProto
+from onnxconverter_common.data_types import FloatTensorType
+from onnxmltools import convert_lightgbm, convert_xgboost
+from skl2onnx import convert_sklearn
 from sklearn.linear_model import LinearRegression, LogisticRegression
+from sklearn.neighbors import RadiusNeighborsRegressor
+from xgboost import XGBRegressor
+
+from metalearners import DRLearner
+from metalearners._typing import Params
 
-from metalearners.drlearner import DRLearner
+from .conftest import all_sklearn_regressors
 
 
 def test_adaptive_clipping_smoke(dummy_dataset):
@@ -21,6 +35,106 @@ def test_adaptive_clipping_smoke(dummy_dataset):
     ml.fit(X, y, w)
 
 
+@pytest.mark.parametrize(
+    "treatment_model_factory, onnx_converter",
+    (
+        list(
+            zip(
+                all_sklearn_regressors,
+                repeat(convert_sklearn),
+            )
+        )
+        + [
+            (LGBMRegressor, convert_lightgbm),
+            (XGBRegressor, convert_xgboost),
+        ]
+    ),
+)
+@pytest.mark.parametrize("is_classification", [True, False])
+def test_drlearner_onnx(
+    treatment_model_factory, onnx_converter, is_classification, onnx_dataset
+):
+    supports_categoricals = treatment_model_factory in [
+        LGBMRegressor,
+        # convert_sklearn does not support categoricals https://github.com/onnx/sklearn-onnx/issues/1051
+        # HistGradientBoostingRegressor,
+        # convert_xgboost does not support categoricals https://github.com/onnx/onnxmltools/issues/469#issuecomment-1993880910
+        # XGBRegressor,
+    ]
+    treatment_model_params: Params | None = None
+    if treatment_model_factory == RadiusNeighborsRegressor:
+        treatment_model_params = {"radius": 10}
+
+    X_numerical, X_with_categorical, y_class, y_reg, w = onnx_dataset
+    n_numerical_features = X_numerical.shape[1]
+
+    if supports_categoricals:
+        X = X_with_categorical
+        n_categorical_features = X.shape[1] - n_numerical_features
+    else:
+        X = X_numerical
+        n_categorical_features = 0
+    n_variants = len(np.unique(w))
+    if is_classification:
+        y = y_class
+        nuisance_model_factory = LogisticRegression
+    else:
+        y = y_reg
+        nuisance_model_factory = LinearRegression
+
+    ml = DRLearner(
+        is_classification,
+        n_variants,
+        nuisance_model_factory=nuisance_model_factory,
+        propensity_model_factory=LGBMClassifier,
+        treatment_model_factory=treatment_model_factory,
+        propensity_model_params={"n_estimators": 1},
+        treatment_model_params=treatment_model_params,
+        n_folds=2,
+    )
+    ml.fit(X, y, w)
+
+    necessary_models = ml._necessary_onnx_models()
+    onnx_models: dict[str, list[ModelProto]] = {}
+
+    for model_kind, models in necessary_models.items():
+        onnx_models[model_kind] = []
+        for model in models:
+            onnx_models[model_kind].append(
+                onnx_converter(
+                    model,
+                    initial_types=[
+                        (
+                            "X",
+                            FloatTensorType(
+                                [None, n_numerical_features + n_categorical_features]
+                            ),
+                        )
+                    ],
+                )
+            )
+
+    final = ml._build_onnx(onnx_models)
+    sess = rt.InferenceSession(
+        final.SerializeToString(), providers=rt.get_available_providers()
+    )
+
+    if supports_categoricals:
+        onnx_X = X.to_numpy(np.float32)
+        # This is needed for categoricals as LGBM uses the categorical codes, when
+        # other implementations support categoricals this may need to be changed
+        onnx_X[:, n_numerical_features] = X[n_numerical_features].cat.codes
+        onnx_X[:, n_numerical_features + 1] = X[n_numerical_features + 1].cat.codes
+    else:
+        onnx_X = X.astype(np.float32)
+
+    (pred_onnx,) = sess.run(
+        ["tau"],
+        {"X": onnx_X},
+    )
+    np.testing.assert_allclose(ml.predict(X, True, "overall"), pred_onnx, atol=5e-4)
+
+
 def test_treatment_effect(
     numerical_experiment_dataset_continuous_outcome_binary_treatment_linear_te,
 ):
@@ -36,5 +150,5 @@ def test_treatment_effect(
         n_folds=2,
     )
     ml.fit_all_nuisance(X, Y, W)
-    est = ml.treatment_effect(X, Y, W)
+    est = ml.average_treatment_effect(X, Y, W, is_oos=False)
     np.testing.assert_almost_equal(est[:, 0], tau.mean(), decimal=1)
diff --git a/tests/test_metalearner.py b/tests/test_metalearner.py
index a65087cd..5721f5c6 100644
--- a/tests/test_metalearner.py
+++ b/tests/test_metalearner.py
@@ -1,6 +1,7 @@
 # Copyright (c) QuantCo 2024-2024
 # SPDX-License-Identifier: BSD-3-Clause
 
+from collections.abc import Mapping, Sequence
 from itertools import chain
 
 import matplotlib.pyplot as plt
@@ -12,6 +13,7 @@
 from sklearn.base import BaseEstimator
 from sklearn.linear_model import LinearRegression, LogisticRegression
 
+from metalearners._typing import _ScikitModel
 from metalearners.cross_fit_estimator import CrossFitEstimator
 from metalearners.data_generation import insert_missing
 from metalearners.drlearner import DRLearner
@@ -111,6 +113,12 @@ def evaluate(self, X, y, w, is_oos, oos_method=None):
     def predict_conditional_average_outcomes(self, X, is_oos, oos_method=None):
         return np.zeros((len(X), 2, 1))
 
+    def _build_onnx(self, models: Mapping[str, Sequence], output_name: str = "tau"): ...
+
+    @classmethod
+    def _necessary_onnx_models(cls) -> dict[str, list[_ScikitModel]]:
+        return {}
+
 
 @pytest.mark.parametrize("is_classification", [True, False])
 @pytest.mark.parametrize("nuisance_model_params", [None, {}, {"n_estimators": 5}])
diff --git a/tests/test_rlearner.py b/tests/test_rlearner.py
index 69499e60..0455f60b 100644
--- a/tests/test_rlearner.py
+++ b/tests/test_rlearner.py
@@ -1,11 +1,24 @@
 # Copyright (c) QuantCo 2024-2024
 # SPDX-License-Identifier: BSD-3-Clause
 
+from itertools import repeat
+
 import numpy as np
+import onnxruntime as rt
 import pandas as pd
 import pytest
+from lightgbm import LGBMClassifier, LGBMRegressor
+from onnx import ModelProto
+from onnxconverter_common.data_types import FloatTensorType
+from onnxmltools import convert_lightgbm, convert_xgboost
+from skl2onnx import convert_sklearn
+from sklearn.linear_model import LinearRegression, LogisticRegression
+from xgboost import XGBRegressor
+
+from metalearners._utils import function_has_argument
+from metalearners.rlearner import RLearner, r_loss
 
-from metalearners.rlearner import r_loss
+from .conftest import all_sklearn_regressors
 
 
 @pytest.mark.parametrize("use_pandas", [True, False])
@@ -26,3 +39,102 @@ def test_r_loss(use_pandas):
         propensity_scores=propensity_scores,
     )
     assert result == pytest.approx(2, abs=1e-4, rel=1e-4)
+
+
+@pytest.mark.parametrize(
+    "treatment_model_factory, onnx_converter",
+    (
+        list(
+            zip(
+                all_sklearn_regressors,
+                repeat(convert_sklearn),
+            )
+        )
+        + [
+            (LGBMRegressor, convert_lightgbm),
+            (XGBRegressor, convert_xgboost),
+        ]
+    ),
+)
+@pytest.mark.parametrize("is_classification", [True, False])
+def test_rlearner_onnx(
+    treatment_model_factory, onnx_converter, is_classification, onnx_dataset
+):
+    if not function_has_argument(treatment_model_factory.fit, "sample_weight"):
+        pytest.skip()
+
+    supports_categoricals = treatment_model_factory in [
+        LGBMRegressor,
+        # convert_sklearn does not support categoricals https://github.com/onnx/sklearn-onnx/issues/1051
+        # HistGradientBoostingRegressor,
+        # convert_xgboost does not support categoricals https://github.com/onnx/onnxmltools/issues/469#issuecomment-1993880910
+        # XGBRegressor,
+    ]
+
+    X_numerical, X_with_categorical, y_class, y_reg, w = onnx_dataset
+    n_numerical_features = X_numerical.shape[1]
+
+    if supports_categoricals:
+        X = X_with_categorical
+        n_categorical_features = X.shape[1] - n_numerical_features
+    else:
+        X = X_numerical
+        n_categorical_features = 0
+    n_variants = len(np.unique(w))
+    if is_classification:
+        y = y_class
+        nuisance_model_factory = LogisticRegression
+    else:
+        y = y_reg
+        nuisance_model_factory = LinearRegression
+
+    ml = RLearner(
+        is_classification,
+        n_variants,
+        nuisance_model_factory=nuisance_model_factory,
+        propensity_model_factory=LGBMClassifier,
+        treatment_model_factory=treatment_model_factory,
+        propensity_model_params={"n_estimators": 1},
+        n_folds=2,
+    )
+    ml.fit(X, y, w)
+
+    necessary_models = ml._necessary_onnx_models()
+    onnx_models: dict[str, list[ModelProto]] = {}
+
+    for model_kind, models in necessary_models.items():
+        onnx_models[model_kind] = []
+        for model in models:
+            onnx_models[model_kind].append(
+                onnx_converter(
+                    model,
+                    initial_types=[
+                        (
+                            "X",
+                            FloatTensorType(
+                                [None, n_numerical_features + n_categorical_features]
+                            ),
+                        )
+                    ],
+                )
+            )
+
+    final = ml._build_onnx(onnx_models)
+    sess = rt.InferenceSession(
+        final.SerializeToString(), providers=rt.get_available_providers()
+    )
+
+    if supports_categoricals:
+        onnx_X = X.to_numpy(np.float32)
+        # This is needed for categoricals as LGBM uses the categorical codes, when
+        # other implementations support categoricals this may need to be changed
+        onnx_X[:, n_numerical_features] = X[n_numerical_features].cat.codes
+        onnx_X[:, n_numerical_features + 1] = X[n_numerical_features + 1].cat.codes
+    else:
+        onnx_X = X.astype(np.float32)
+
+    (pred_onnx,) = sess.run(
+        ["tau"],
+        {"X": onnx_X},
+    )
+    np.testing.assert_allclose(ml.predict(X, True, "overall"), pred_onnx, atol=5e-4)
diff --git a/tests/test_tlearner.py b/tests/test_tlearner.py
new file mode 100644
index 00000000..9c8d33f0
--- /dev/null
+++ b/tests/test_tlearner.py
@@ -0,0 +1,141 @@
+# Copyright (c) QuantCo 2024-2024
+# SPDX-License-Identifier: BSD-3-Clause
+
+from functools import partial
+from itertools import repeat
+
+import numpy as np
+import onnxruntime as rt
+import pytest
+from lightgbm import LGBMClassifier, LGBMRegressor
+from onnx import ModelProto
+from onnxconverter_common.data_types import FloatTensorType
+from onnxmltools import convert_lightgbm, convert_xgboost
+from skl2onnx.convert import convert_sklearn
+from sklearn.ensemble import (
+    HistGradientBoostingClassifier,
+    HistGradientBoostingRegressor,
+)
+from sklearn.neighbors import (
+    RadiusNeighborsClassifier,
+    RadiusNeighborsRegressor,
+)
+from xgboost import XGBClassifier, XGBRegressor
+
+from metalearners import TLearner
+from metalearners._typing import Params
+
+from .conftest import all_sklearn_classifiers, all_sklearn_regressors
+
+
+@pytest.mark.parametrize(
+    "nuisance_model_factory, onnx_converter, is_classification",
+    (
+        list(
+            zip(
+                all_sklearn_classifiers,
+                repeat(partial(convert_sklearn, options={"zipmap": False})),
+                repeat([True]),
+            )
+        )
+        + list(zip(all_sklearn_regressors, repeat(convert_sklearn), repeat(False)))
+        + [
+            (LGBMClassifier, partial(convert_lightgbm, zipmap=False), True),
+            (LGBMRegressor, convert_lightgbm, False),
+            (XGBClassifier, convert_xgboost, True),
+            (XGBRegressor, convert_xgboost, False),
+        ]
+    ),
+)
+def test_tlearner_onnx(
+    nuisance_model_factory, onnx_converter, is_classification, onnx_dataset
+):
+    supports_categoricals = nuisance_model_factory in [
+        LGBMClassifier,
+        LGBMRegressor,
+        # convert_sklearn does not support categoricals https://github.com/onnx/sklearn-onnx/issues/1051
+        # HistGradientBoostingClassifier,
+        # HistGradientBoostingRegressor,
+        # convert_xgboost does not support categoricals https://github.com/onnx/onnxmltools/issues/469#issuecomment-1993880910
+        # XGBClassifier,
+        # XGBRegressor,
+    ]
+
+    nuisance_model_params: Params | None
+    if nuisance_model_factory in [RadiusNeighborsClassifier, RadiusNeighborsRegressor]:
+        nuisance_model_params = {"radius": 10}
+    elif nuisance_model_factory in [
+        HistGradientBoostingClassifier,
+        HistGradientBoostingRegressor,
+    ]:
+        # This is unnecessary for now but if at some point convert_sklearn supports categoricals this is needed
+        nuisance_model_params = {"categorical_features": "from_dtype"}
+    elif nuisance_model_factory in [XGBClassifier, XGBRegressor]:
+        # This is unnecessary for now but if at some point convert_xgboost supports categoricals this is needed
+        nuisance_model_params = {"enable_categorical": True}
+    else:
+        nuisance_model_params = None
+
+    X_numerical, X_with_categorical, y_class, y_reg, w = onnx_dataset
+    n_numerical_features = X_numerical.shape[1]
+
+    if supports_categoricals:
+        X = X_with_categorical
+        n_categorical_features = X.shape[1] - n_numerical_features
+    else:
+        X = X_numerical
+        n_categorical_features = 0
+    n_variants = len(np.unique(w))
+    if is_classification:
+        y = y_class
+    else:
+        y = y_reg
+
+    ml = TLearner(
+        is_classification,
+        n_variants,
+        nuisance_model_factory=nuisance_model_factory,
+        nuisance_model_params=nuisance_model_params,
+        n_folds=2,
+    )
+    ml.fit(X, y, w)
+
+    necessary_models = ml._necessary_onnx_models()
+    onnx_models: dict[str, list[ModelProto]] = {}
+
+    for model_kind, models in necessary_models.items():
+        onnx_models[model_kind] = []
+        for model in models:
+            onnx_models[model_kind].append(
+                onnx_converter(
+                    model,
+                    initial_types=[
+                        (
+                            "X",
+                            FloatTensorType(
+                                [None, n_numerical_features + n_categorical_features]
+                            ),
+                        )
+                    ],
+                )
+            )
+
+    final = ml._build_onnx(onnx_models)
+    sess = rt.InferenceSession(
+        final.SerializeToString(), providers=rt.get_available_providers()
+    )
+
+    if supports_categoricals:
+        onnx_X = X.to_numpy(np.float32)
+        # This is needed for categoricals as LGBM uses the categorical codes, when
+        # other implementations support categoricals this may need to be changed
+        for i in range(n_categorical_features):
+            onnx_X[:, n_numerical_features + i] = X[n_numerical_features + i].cat.codes
+    else:
+        onnx_X = X.astype(np.float32)
+
+    (pred_onnx,) = sess.run(
+        ["tau"],
+        {"X": onnx_X},
+    )
+    np.testing.assert_allclose(ml.predict(X, True, "overall"), pred_onnx, atol=5e-4)
diff --git a/tests/test_utils.py b/tests/test_utils.py
index c3f334ec..d88b5d14 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -2,11 +2,16 @@
 # SPDX-License-Identifier: BSD-3-Clause
 
 import numpy as np
+import pandas as pd
 import pytest
 from lightgbm import LGBMRegressor
 
 from metalearners.metalearner import MetaLearner
-from metalearners.utils import metalearner_factory, simplify_output
+from metalearners.utils import (
+    FixedBinaryPropensity,
+    metalearner_factory,
+    simplify_output,
+)
 
 
 @pytest.mark.parametrize("prefix", ["T"])
@@ -52,3 +57,57 @@ def test_simplify_output(input, expected):
 def test_simplify_output_raises(input):
     with pytest.raises(ValueError, match="needs to be 3-dimensional"):
         simplify_output(input)
+
+
+@pytest.mark.parametrize("use_pd", [True, False])
+def test_fixed_binary_propensity(use_pd):
+    propensity_score = 0.3
+    dominant_class = propensity_score >= 0.5
+
+    model = FixedBinaryPropensity(propensity_score=propensity_score)
+
+    n_samples = 5
+    X_train = np.ones((n_samples, 5))
+    y_train = np.ones(n_samples)
+    if use_pd:
+        X_train = pd.DataFrame(X_train)
+        y_train = pd.Series(y_train)
+
+    model.fit(X_train, y_train)
+
+    n_test_samples = 3
+    X_test = np.zeros(n_test_samples)
+
+    class_predictions = model.predict(X_test)
+    assert np.array_equal(
+        class_predictions, np.array(np.ones(n_test_samples) * dominant_class)
+    )
+
+    probability_estimates = model.predict_proba(X_test)
+    assert np.array_equal(
+        probability_estimates,
+        np.column_stack(
+            (
+                np.ones(n_test_samples) * (1 - propensity_score),
+                np.ones(n_test_samples) * propensity_score,
+            )
+        ),
+    )
+
+
+@pytest.mark.parametrize("propensity_score", [-1, 100, 1.1])
+def test_fixed_binary_propensity_not_a_propbability(propensity_score):
+    with pytest.raises(ValueError, match="between 0 and 1 but got"):
+        FixedBinaryPropensity(propensity_score=propensity_score)
+
+
+def test_fixed_binary_propensity_non_binary():
+    propensity_score = 0.3
+
+    model = FixedBinaryPropensity(propensity_score=propensity_score)
+
+    n_samples = 5
+    X_train = np.ones((n_samples, 5))
+    y_train = np.fromiter(range(n_samples), dtype=int)
+    with pytest.raises(ValueError, match="only supports binary outcomes"):
+        model.fit(X_train, y_train)
diff --git a/tests/test_xlearner.py b/tests/test_xlearner.py
new file mode 100644
index 00000000..64e22395
--- /dev/null
+++ b/tests/test_xlearner.py
@@ -0,0 +1,136 @@
+# Copyright (c) QuantCo 2024-2024
+# SPDX-License-Identifier: BSD-3-Clause
+
+import random
+from functools import partial
+from itertools import repeat
+
+import numpy as np
+import onnxruntime as rt
+import pytest
+from lightgbm import LGBMClassifier, LGBMRegressor
+from onnx import ModelProto
+from onnxconverter_common.data_types import FloatTensorType
+from onnxmltools import convert_lightgbm, convert_xgboost
+from skl2onnx import convert_sklearn
+from sklearn.neighbors import RadiusNeighborsClassifier, RadiusNeighborsRegressor
+from xgboost import XGBClassifier, XGBRegressor
+
+from metalearners import XLearner
+from metalearners._typing import Params
+from metalearners.xlearner import CONTROL_EFFECT_MODEL, TREATMENT_EFFECT_MODEL
+
+from .conftest import all_sklearn_classifiers, all_sklearn_regressors
+
+
+@pytest.mark.parametrize(
+    "treatment_model_factory, treatment_onnx_converter",
+    random.sample(  # With this we run a different set of tests every time as running all of them is too costly (there are ~50 regressors and classifiers)
+        list(
+            zip(
+                all_sklearn_regressors,
+                repeat(convert_sklearn),
+            )
+        )
+        + [
+            (LGBMRegressor, convert_lightgbm),
+            (XGBRegressor, convert_xgboost),
+        ],
+        4,
+    ),
+)
+@pytest.mark.parametrize(
+    "propensity_model_factory, propensity_onnx_converter",
+    random.sample(
+        list(
+            zip(
+                all_sklearn_classifiers,
+                [partial(convert_sklearn, options={"zipmap": False})]
+                * len(all_sklearn_classifiers),
+            )
+        )
+        + [
+            (LGBMClassifier, partial(convert_lightgbm, zipmap=False)),
+            (XGBClassifier, convert_xgboost),
+        ],
+        4,
+    ),
+)
+@pytest.mark.parametrize("is_classification", [True, False])
+def test_xlearner_onnx(
+    treatment_model_factory,
+    propensity_model_factory,
+    treatment_onnx_converter,
+    propensity_onnx_converter,
+    is_classification,
+    onnx_dataset,
+):
+    treatment_model_params: Params | None
+    if treatment_model_factory == RadiusNeighborsRegressor:
+        treatment_model_params = {"radius": 10}
+    else:
+        treatment_model_params = None
+    propensity_model_params: Params | None
+    if propensity_model_factory == RadiusNeighborsClassifier:
+        propensity_model_params = {"radius": 10}
+    else:
+        propensity_model_params = None
+
+    X, _, y_class, y_reg, w = onnx_dataset
+    n_numerical_features = X.shape[1]
+    n_variants = len(np.unique(w))
+    if is_classification:
+        y = y_class
+        nuisance_model_factory = LGBMClassifier
+    else:
+        y = y_reg
+        nuisance_model_factory = LGBMRegressor
+
+    nuisance_model_params = {"n_estimators": 1}
+
+    ml = XLearner(
+        is_classification,
+        n_variants,
+        nuisance_model_factory=nuisance_model_factory,
+        nuisance_model_params=nuisance_model_params,
+        propensity_model_factory=propensity_model_factory,
+        propensity_model_params=propensity_model_params,
+        treatment_model_factory=treatment_model_factory,
+        treatment_model_params=treatment_model_params,
+        n_folds=2,
+    )
+    ml.fit(X, y, w)
+
+    necessary_models = ml._necessary_onnx_models()
+    onnx_models: dict[str, list[ModelProto]] = {}
+
+    for model_kind, models in necessary_models.items():
+        onnx_models[model_kind] = []
+        if model_kind in [CONTROL_EFFECT_MODEL, TREATMENT_EFFECT_MODEL]:
+            onnx_converter = treatment_onnx_converter
+        else:
+            onnx_converter = propensity_onnx_converter
+        for model in models:
+            onnx_models[model_kind].append(
+                onnx_converter(
+                    model,
+                    initial_types=[
+                        (
+                            "X",
+                            FloatTensorType([None, n_numerical_features]),
+                        )
+                    ],
+                )
+            )
+
+    final = ml._build_onnx(onnx_models)
+
+    sess = rt.InferenceSession(
+        final.SerializeToString(), providers=rt.get_available_providers()
+    )
+
+    (pred_onnx,) = sess.run(
+        ["tau"],
+        {"X": X.astype(np.float32)},
+    )
+    np.testing.assert_allclose(ml.predict(X, True, "overall"), pred_onnx, atol=5e-4)