diff --git a/CHANGELOG.rst b/CHANGELOG.rst
index 97802eb5..ad678eba 100644
--- a/CHANGELOG.rst
+++ b/CHANGELOG.rst
@@ -22,6 +22,14 @@ Changelog
 
 * 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.
+
 0.8.0 (2024-07-22)
 ------------------
 
diff --git a/docs/examples/example_estimating_ates.ipynb b/docs/examples/example_estimating_ates.ipynb
new file mode 100644
index 00000000..72011e58
--- /dev/null
+++ b/docs/examples/example_estimating_ates.ipynb
@@ -0,0 +1,580 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Example: Estimating Average Treatment Effects\n",
+    "=============================\n",
+    "\n",
+    "Motivation\n",
+    "----------\n",
+    "\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 `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",
+    "-------"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import statsmodels.formula.api as smf\n",
+    "\n",
+    "np.random.seed(42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Binary Treatment with confounding"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def binary_dgp(n, k, pscore_fn, tau_fn, outcome_fn, k_cat=1):\n",
+    "    \"\"\"DGP for a confounded treatment assignment dgp\n",
+    "\n",
+    "    Args:\n",
+    "        n (int): sample size\n",
+    "        k (int): number of continuous covariates\n",
+    "        pscore_fn (lambda): propensity score function\n",
+    "        tau_fn (lambda): treatment effect function. Can be scalar for constant effect.\n",
+    "        outcome_fn (lambda): outcome DGP\n",
+    "    \"\"\"\n",
+    "    Sigma = np.random.uniform(-1, 1, (k, k))\n",
+    "    Sigma = Sigma @ Sigma.T\n",
+    "    Xnum = np.random.multivariate_normal(np.zeros(k), Sigma, n)\n",
+    "    # generate categorical variables\n",
+    "    Xcat = np.random.binomial(1, 0.5, (n, k_cat))\n",
+    "    X = np.c_[Xnum, Xcat]\n",
+    "    W = np.random.binomial(1, pscore_fn(X), n)\n",
+    "    Y = outcome_fn(X, W, tau_fn)\n",
+    "    df = pd.DataFrame(\n",
+    "        np.c_[Y, W, X], columns=[\"Y\", \"W\"] + [f\"X{i}\" for i in range(k + 1)]\n",
+    "    )\n",
+    "    return df\n",
+    "\n",
+    "\n",
+    "def outcome_fn(x, w, taufn):\n",
+    "    return (\n",
+    "        taufn(x) * w\n",
+    "        + x[:, 0]\n",
+    "        + 2 * x[:, 1] ** 2\n",
+    "        + 3 * x[:, 3] * x[:, 1]\n",
+    "        + x[:, 2]\n",
+    "        + x[:, 3]\n",
+    "        + np.random.normal(0, 1, n)\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "n, k = 10_000, 3\n",
+    "pscore_fn = lambda x: 1 / (1 + np.exp(-x[:, 0] - x[:, 1] - x[:, 2] ** 2 + x[:, 3]))\n",
+    "# simulate constant_effects\n",
+    "# tau_fn = lambda x: 1 + 2 * x[:, 0] + 3 * x[:, 1] + 4 * x[:, 2] + 5 * x[:, 3]\n",
+    "tau_fn = lambda x: 1\n",
+    "df = binary_dgp(n, k, pscore_fn, tau_fn=tau_fn, outcome_fn=outcome_fn)\n",
+    "outcome_column, treatment_column = \"Y\", \"W\"\n",
+    "feature_columns = [f\"X{i}\" for i in range(k + 1)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Linear Regression "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "naive_lm = smf.ols(f\"{outcome_column} ~ {treatment_column}\", df) .fit(cov_type=\"HC1\")\n",
+    "naive_est = naive_lm.params.iloc[1], naive_lm.bse.iloc[1]\n",
+    "naive_est"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "covaradjust_lm = smf.ols(f\"{outcome_column} ~ {treatment_column}+{'+'.join(feature_columns)}\",\n",
+    "                   df) .fit(cov_type=\"HC1\")\n",
+    "linreg_est = covaradjust_lm.params.iloc[1], covaradjust_lm.bse.iloc[1]\n",
+    "linreg_est"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Linear model is misspecified, so both the naive and conditional estimates are biased."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### `metalearners`: `DRLearner`\n",
+    "\n",
+    "Point estimates and standard errors for treatment effects for the AIPW estimator can be computed by aggregating the pseudo-outcome computed by the `DRLearner` class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from metalearners import DRLearner\n",
+    "from lightgbm import LGBMRegressor, LGBMClassifier\n",
+    "from sklearn.dummy import DummyRegressor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metalearners_dr = DRLearner(\n",
+    "    nuisance_model_factory=LGBMRegressor,\n",
+    "    treatment_model_factory=DummyRegressor, # not actually used since we don't fit treatment model\n",
+    "    propensity_model_factory=LGBMClassifier,\n",
+    "    is_classification=False,\n",
+    "    n_variants=2,\n",
+    "    nuisance_model_params={\"verbose\": -1},\n",
+    "    propensity_model_params={\"verbose\": -1},\n",
+    ")\n",
+    "\n",
+    "metalearners_dr.fit_all_nuisance(\n",
+    "    X=df[feature_columns],\n",
+    "    y=df[outcome_column],\n",
+    "    w=df[treatment_column],\n",
+    ")\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"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gamma_i = metalearners_dr._pseudo_outcome(\n",
+    "    X=df[feature_columns],\n",
+    "    w=df[treatment_column],\n",
+    "    y=df[outcome_column],\n",
+    "    treatment_variant=1,\n",
+    "    is_oos=False,\n",
+    ")\n",
+    "gamma_i.mean(), gamma_i.std()/np.sqrt(n)\n",
+    "est, se = gamma_i.mean(), gamma_i.std()/np.sqrt(n)\n",
+    "print(f\"est: {est}, se: {se}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### `doubleml`: `DoubleMLIRM`\n",
+    "\n",
+    "The [`doubleML`](https://docs.doubleml.org/stable/index.html) library focuses on estimating average effects and has an 'interactive regression model (IRM)' class that estimates the ATE using the same pseudo-outcome method as the `DRLearner` class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from doubleml import DoubleMLIRM, DoubleMLData\n",
+    "dml_data = DoubleMLData(\n",
+    "    df,\n",
+    "    x_cols=feature_columns,\n",
+    "    y_col=outcome_column,\n",
+    "    d_cols=treatment_column,\n",
+    ")\n",
+    "\n",
+    "aipw_mod = DoubleMLIRM(\n",
+    "    dml_data,\n",
+    "    ml_g = LGBMRegressor(),\n",
+    "    ml_m = LGBMClassifier(),\n",
+    "    n_folds=5,\n",
+    ")\n",
+    "\n",
+    "aipw_mod.fit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(doubleml_est := aipw_mod.summary.values[0, :2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### `econML`: `LinearDRLearner`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from econml.dr import LinearDRLearner\n",
+    "import formulaic as fm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(ff := f\"{outcome_column} ~ 0 + {'+'.join(feature_columns)}\")\n",
+    "y, X = fm.Formula(ff).get_model_matrix(df, output=\"numpy\")\n",
+    "W = df[treatment_column].values[:, np.newaxis]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "econml_dr = LinearDRLearner(model_regression=LGBMRegressor(), model_propensity=LGBMClassifier())\n",
+    "econml_dr.fit(y, T=W, W=X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(econml_est := econml_dr.intercept__inference(1).summary_frame().iloc[0, :2].values)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### comparison\n",
+    "\n",
+    "All ml-based estimators yield comparable results (both point estimate and standard error)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pd.DataFrame(\n",
+    " np.c_[\n",
+    "    naive_est,\n",
+    "    linreg_est,\n",
+    "    metalearners_est.flatten(),\n",
+    "    doubleml_est,\n",
+    "    econml_est,\n",
+    "], index = ['est', 'se'],\n",
+    "columns = ['naive', 'linreg', 'metalearners', 'doubleml', 'econml']\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Multiple Treatment Arms\n",
+    "\n",
+    "Next, we demonstrate the use of the `treatment_effect` method of the `DRLearner` class in a setting with multiple discrete treatments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.seed(123)\n",
+    "\n",
+    "def multi_pscore_fn(x, num_treatments=3):\n",
+    "    logits = np.zeros((x.shape[0], num_treatments))\n",
+    "    logits[:, 0] = -x[:, 0] - x[:, 1] - x[:, 2]  + x[:, 3]\n",
+    "    logits[:, 1] = -x[:, 1] - x[:, 2] - x[:, 3]  + x[:, 0]\n",
+    "    logits[:, 2] = -x[:, 2] - x[:, 3] - x[:, 0]  + x[:, 1]\n",
+    "    # Apply softmax\n",
+    "    exp_logits = np.exp(logits - np.max(logits, axis=1, keepdims=True))\n",
+    "    pscores = exp_logits / np.sum(exp_logits, axis=1, keepdims=True)\n",
+    "    return pscores\n",
+    "\n",
+    "# And then modify the multi_treatment_dgp function to use these probabilities:\n",
+    "\n",
+    "def multi_treatment_dgp(n, k, pscore_fn, tau_fn, outcome_fn, k_cat=1, num_treatments=3):\n",
+    "    \"\"\"DGP for a confounded multiple treatment assignment\"\"\"\n",
+    "    Sigma = np.random.uniform(-1, 1, (k, k))\n",
+    "    Sigma = Sigma @ Sigma.T\n",
+    "    Xnum = np.random.multivariate_normal(np.zeros(k), Sigma, n)\n",
+    "    Xcat = np.random.binomial(1, 0.5, (n, k_cat))\n",
+    "    X = np.c_[Xnum, Xcat]\n",
+    "\n",
+    "    # Generate multiple treatments\n",
+    "    pscores = pscore_fn(X, num_treatments)\n",
+    "    W = np.zeros((n, num_treatments), dtype=int)\n",
+    "    for i in range(n):\n",
+    "        W[i, np.random.choice(num_treatments, p=pscores[i])] = 1\n",
+    "    Y = outcome_fn(X, W, tau_fn)\n",
+    "    df = pd.DataFrame(\n",
+    "        np.c_[Y,  X],\n",
+    "        columns=[\"Y\"]\n",
+    "        + [f\"X{i}\" for i in range(k + 1)],\n",
+    "    ).assign(\n",
+    "        W=np.argmax(W, axis=1)\n",
+    "    )  # convert one-hot encoding to single column\n",
+    "    return df\n",
+    "\n",
+    "\n",
+    "def multi_outcome_fn(x, w, taufn):\n",
+    "    return (\n",
+    "        np.sum(taufn(x) * w, axis=1)  # sum over treatments\n",
+    "        + x[:, 0]\n",
+    "        + 2 * x[:, 1] ** 2\n",
+    "        + 3 * x[:, 3] * x[:, 1]\n",
+    "        + x[:, 2]\n",
+    "        + x[:, 3]\n",
+    "        + np.random.normal(0, 1, n)\n",
+    "    )\n",
+    "\n",
+    "def multi_tau_fn(x):\n",
+    "    return np.c_[ 0, 1, 2]\n",
+    "\n",
+    "n, k = 10_000, 3\n",
+    "\n",
+    "df_multi = multi_treatment_dgp(\n",
+    "    n, k, multi_pscore_fn, tau_fn=multi_tau_fn, outcome_fn=multi_outcome_fn\n",
+    ")\n",
+    "feature_columns = [f\"X{i}\" for i in range(k + 1)]\n",
+    "outcome_column, treatment_column = \"Y\", \"W\"\n",
+    "df_multi.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lm_multi = smf.ols(\"Y ~ C(W)\", df_multi).fit()\n",
+    "lm_multi.params"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These estimates are substantially biased, since the model is misspecified (the true outcome and propensity score contains quadratic terms)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metalearners_dr_2 = DRLearner(\n",
+    "    nuisance_model_factory=LGBMRegressor,\n",
+    "    treatment_model_factory=DummyRegressor, # not actually used since we don't fit treatment model\n",
+    "    propensity_model_factory=LGBMClassifier,\n",
+    "    is_classification=False,\n",
+    "    n_variants=3,\n",
+    "    nuisance_model_params={\"verbose\": -1},\n",
+    "    propensity_model_params={\"verbose\": -1},\n",
+    ")\n",
+    "\n",
+    "metalearners_dr_2.fit_all_nuisance(\n",
+    "    X=df_multi[feature_columns],\n",
+    "    y=df_multi[outcome_column],\n",
+    "    w=df_multi[treatment_column],\n",
+    ")\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"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These estimates are less biased than those produced by the linear model."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Binary Outcome\n",
+    "\n",
+    "Finally, we consider a case where the outcome is binary, in which case the DRLearner class is initialized with `is_classification=True` and the nuisance model is a classifier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def classification_dgp(n, k, pscore_fn, tau_fn, outcome_fn, k_cat=1):\n",
+    "    \"\"\"DGP for a confounded treatment assignment with binary outcome\"\"\"\n",
+    "    Sigma = np.random.uniform(-1, 1, (k, k))\n",
+    "    Sigma = Sigma @ Sigma.T\n",
+    "    Xnum = np.random.multivariate_normal(np.zeros(k), Sigma, n)\n",
+    "    Xcat = np.random.binomial(1, 0.5, (n, k_cat))\n",
+    "    X = np.c_[Xnum, Xcat]\n",
+    "    W = np.random.binomial(1, pscore_fn(X), n)\n",
+    "    Y_prob = outcome_fn(X, W, tau_fn)\n",
+    "    Y = np.random.binomial(1, Y_prob).astype(int)\n",
+    "    df = pd.DataFrame(\n",
+    "        np.c_[Y, W, X], columns=[\"Y\", \"W\"] + [f\"X{i}\" for i in range(k + 1)]\n",
+    "    ).assign(Y = pd.to_numeric(Y, downcast='integer'))\n",
+    "    return df\n",
+    "\n",
+    "\n",
+    "def classification_outcome_fn(x, w, taufn):\n",
+    "    logits = (\n",
+    "        taufn(x) * w\n",
+    "        + x[:, 0]\n",
+    "        + 2 * x[:, 1]\n",
+    "        + 3 * x[:, 3] * x[:, 1]\n",
+    "        + x[:, 2]\n",
+    "        + x[:, 3]\n",
+    "    )\n",
+    "    return 1 / (1 + np.exp(-logits))\n",
+    "\n",
+    "\n",
+    "# Propensity score function\n",
+    "pscore_fn = lambda x: 1 / (1 + np.exp(-x[:, 0] - x[:, 1] - x[:, 2] ** 2 + x[:, 3]))\n",
+    "\n",
+    "# Treatment effect function - constant increase in probability of Y=1\n",
+    "classification_tau_fn = lambda x: 0.05\n",
+    "\n",
+    "n, k = 10_000, 3\n",
+    "df_class = classification_dgp(\n",
+    "    n, k, pscore_fn, tau_fn=classification_tau_fn, outcome_fn=classification_outcome_fn\n",
+    ")\n",
+    "feature_columns = [f\"X{i}\" for i in range(k + 1)]\n",
+    "outcome_column, treatment_column = \"Y\", \"W\"\n",
+    "\n",
+    "df_class.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# naive TE\n",
+    "df_class.groupby(\"W\")[\"Y\"].mean().diff()[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metalearners_dr_3 = DRLearner(\n",
+    "    nuisance_model_factory=LGBMClassifier,\n",
+    "    treatment_model_factory=DummyRegressor, # not actually used since we don't fit treatment model\n",
+    "    propensity_model_factory=LGBMClassifier,\n",
+    "    is_classification=True,\n",
+    "    n_variants=2,\n",
+    "    nuisance_model_params={\"verbose\": -1},\n",
+    "    propensity_model_params={\"verbose\": -1},\n",
+    ")\n",
+    "\n",
+    "metalearners_dr_3.fit_all_nuisance(\n",
+    "    X=df_class[feature_columns],\n",
+    "    y=df_class[outcome_column],\n",
+    "    w=df_class[treatment_column],\n",
+    ")\n",
+    "metalearners_est_3 = metalearners_dr_3.average_treatment_effect( # still need to pass data objects since DRLearner does not retain any data\n",
+    "    X=df_class[feature_columns],\n",
+    "    y=df_class[outcome_column],\n",
+    "    w=df_class[treatment_column],\n",
+    "    is_oos=False,\n",
+    ")\n",
+    "metalearners_est_3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Again, we have lower bias than the naive comparison."
+   ]
+  }
+ ],
+ "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": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/examples/index.rst b/docs/examples/index.rst
index 429c4ce3..b91cea15 100644
--- a/docs/examples/index.rst
+++ b/docs/examples/index.rst
@@ -6,6 +6,7 @@ Examples
    :maxdepth: 1
 
    Estimating CATEs with MetaLearners <example_basic.ipynb>
+   Estimating ATEs with DRLearner <example_estimating_ates.ipynb>
    Reusing base models <example_reuse.ipynb>
    Explainability: Lime plots of MetaLearners <example_lime.ipynb>
    Explainability: Feature importance and SHAP values <example_feature_importance_shap.ipynb>
diff --git a/metalearners/drlearner.py b/metalearners/drlearner.py
index 8ec4b7e3..1bfc2ab5 100644
--- a/metalearners/drlearner.py
+++ b/metalearners/drlearner.py
@@ -198,7 +198,7 @@ def fit_all_nuisance(
         )
 
         self._assign_joblib_nuisance_results(results)
-
+        self._nuisance_models_fit = True
         return self
 
     def fit_all_treatment(
@@ -338,6 +338,45 @@ def evaluate(
 
         return variant_outcome_evaluation | propensity_evaluation | treatment_evaluation
 
+    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-
+        stage treatment model: it uses the pseudo-outcome alone and computes the average
+        and SE. Can be used following the
+        :meth:`~metalearners.drlearner.DRLearner.fit_all_nuisance` method.
+
+        Args:
+            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.
+        """
+        if not self._nuisance_models_fit:
+            raise ValueError(
+                "The nuisance models need to be fitted before computing the treatment effect."
+            )
+        gamma_matrix = np.zeros((len(X), self.n_variants - 1))
+        for treatment_variant in range(1, self.n_variants):
+            gamma_matrix[:, treatment_variant - 1] = self._pseudo_outcome(
+                X=X,
+                w=w,
+                y=y,
+                treatment_variant=treatment_variant,
+                is_oos=is_oos,
+            )
+        treatment_effect = gamma_matrix.mean(axis=0)
+        standard_error = gamma_matrix.std(axis=0) / np.sqrt(len(X))
+        return np.c_[treatment_effect, standard_error]
+
     def _pseudo_outcome(
         self,
         X: Matrix,
diff --git a/pixi.lock b/pixi.lock
index 25f7b561..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
@@ -40,16 +40,15 @@ 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
       - conda: https://conda.anaconda.org/conda-forge/linux-64/graphite2-1.3.13-h59595ed_1003.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/graphviz-11.0.0-hc68bbd7_0.conda
+      - 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
@@ -58,16 +57,16 @@ 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
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-23_linux64_openblas.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.20-hd590300_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.21-h4bc722e_0.conda
       - 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
@@ -83,22 +82,21 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/librsvg-2.58.2-h9564881_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.46.0-hde9e2c9_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-14.1.0-hc0a3c3a_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.6.0-h1dd3fc0_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.6.0-h46a8edc_4.conda
       - 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-2.0.3-cpu_h7afb2cf_4.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/libxgboost-2.1.1-cpu_h7afb2cf_1.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-4.4.0-cpu_py3.10h255c789_3.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
       - conda: https://conda.anaconda.org/conda-forge/linux-64/markupsafe-2.1.5-py310h2372a71_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.9.1-py310h0b1de36_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.9.1-py310hf02ac8c_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/mpc-1.3.1-hfe3b2da_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/mpfr-4.2.1-h9458935_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/mpfr-4.2.1-h38ae2d0_2.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/mpmath-1.3.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/multiprocess-0.70.16-py310h2372a71_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyh9f0ad1d_0.tar.bz2
@@ -118,13 +116,13 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/patsy-0.5.6-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/pcre2-10.44-h0f59acf_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/pillow-10.4.0-py310hebfe307_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/pip-24.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/pip-24.2-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/pixman-0.43.2-h59595ed_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pox-0.3.4-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/ppft-1.7.6.8-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/progressbar2-4.4.2-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-h36c2ea0_1001.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/noarch/py-xgboost-2.0.3-cpu_pyh995e691_4.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/py-xgboost-2.1.1-cpu_pyh995e691_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/pydot-3.0.1-py310hff52083_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pydotplus-2.0.2-pyhd8ed1ab_6.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pygam-0.9.1-pyhd8ed1ab_0.conda
@@ -144,7 +142,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/scikit-learn-1.5.1-py310h146d792_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/scipy-1.14.0-py310h93e2701_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/seaborn-base-0.13.2-pyhd8ed1ab_2.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/setuptools-71.0.4-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/setuptools-72.1.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/shap-0.43.0-py310hcc13569_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/six-1.16.0-pyh6c4a22f_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/sleef-3.6.1-h3400bea_1.conda
@@ -156,12 +154,12 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/tabulate-0.9.0-pyhd8ed1ab_1.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.5.0-pyhc1e730c_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_h4845f30_101.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.66.4-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.66.5-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.12.2-pyha770c72_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2024a-h0c530f3_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/unicodedata2-15.1.0-py310h2372a71_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/wheel-0.43.0-pyhd8ed1ab_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/xgboost-2.0.3-cpu_pyhb8f9a19_4.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/wheel-0.44.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/xgboost-2.1.1-cpu_pyhb8f9a19_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-kbproto-1.0.7-h7f98852_1002.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libice-1.1.1-hd590300_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libsm-1.2.4-h7391055_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
@@ -210,16 +208,15 @@ 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
       - conda: https://conda.anaconda.org/conda-forge/osx-64/graphite2-1.3.13-h73e2aa4_1003.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-64/graphviz-11.0.0-hc9017ca_0.conda
+      - 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
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-  license_family: MIT
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-  timestamp: 1695990206327
-- kind: conda
-  name: brotli-python
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-  build: py312h53d5487_1
+  build: py311h12c1d0e_1
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   license: MIT
   license_family: MIT
-  size: 322514
-  timestamp: 1695991054894
+  size: 322086
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-  md5: 1bc01b9ffdf42beb1a9fe4e9222e0567
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-  - python_abi 3.12.* *_cp312
+  - python >=3.11,<3.12.0a0
+  - python >=3.11,<3.12.0a0 *_cpython
+  - python_abi 3.11.* *_cp311
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   license: MIT
   license_family: MIT
-  size: 343435
-  timestamp: 1695990731924
+  size: 343332
+  timestamp: 1695991223439
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+  url: https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.1.0-py311hb755f60_1.conda
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+  license: MIT
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   license_family: BSD
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-  size: 213927
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+  - zstd >=1.5.6,<1.6.0a0
+  license: BSD-3-Clause
+  license_family: BSD
+  size: 280426
+  timestamp: 1722388924129
+- kind: conda
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+  url: https://conda.anaconda.org/conda-forge/linux-64/c-blosc2-2.15.1-hc57e6cf_0.conda
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-  md5: d264e5b9759cab8d203cdfe43eabd8b5
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-  - icu >=73.2,<74.0a0
-  - libcxx >=16
-  - libglib >=2.80.2,<3.0a0
+  - icu >=75.1,<76.0a0
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-  size: 886028
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   license_family: MIT
   size: 137627
   timestamp: 1710362144873
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   version: 2.6.2
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   license_family: MIT
   size: 237668
   timestamp: 1674829263740
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 - kind: conda
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   version: '1'
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   license: LGPL-2.1
   size: 114383
   timestamp: 1604416621168
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   license_family: LGPL
   size: 528149
   timestamp: 1715782983957
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   version: 5.2.2
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   license_family: LGPL
   size: 96855
   timestamp: 1711634169756
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-  - gtk2
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   license: EPL-1.0
   license_family: Other
-  size: 5047991
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   name: graphviz
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+  license: EPL-1.0
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   license: EPL-1.0
   license_family: Other
-  size: 2304422
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   name: graphviz
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   - pango >=1.50.14,<2.0a0
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   license_family: Other
-  size: 5030587
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   license: MIT
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   - graphite2
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   license: MIT
   license_family: MIT
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   name: hpack
   version: 4.0.0
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   license_family: MIT
   size: 12129203
   timestamp: 1720853576813
+- kind: conda
+  name: icu
+  version: '75.1'
+  build: he0c23c2_0
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+  url: https://conda.anaconda.org/conda-forge/win-64/icu-75.1-he0c23c2_0.conda
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-  - 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
@@ -11656,43 +12155,39 @@ packages:
   - openjpeg >=2.5.2,<3.0a0
   - 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
-  sha256: a8f2702756e37c81b0bf0584fc36684d7e18aa789e2c262e9149b5c2d85d663d
-  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
   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
@@ -11702,41 +12197,47 @@ 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
+  - 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
-  sha256: e9219ddb4c00a4bab6278219b17dcfacf8a66f085e40f62fd67b5ea86a99be50
-  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
+  sha256: 8ecc5c82534250844f00c1adcc173e5f5e638a0548b9980c95de0ae46090c973
+  md5: e87f3450ac84bcf1551cae9d24464cf6
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-  - 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
+  - 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
@@ -11747,47 +12248,44 @@ packages:
   - 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
-  sha256: 736975c13598102fd3066b9e1d8b631f0bd933b3ab16a177e60e4ef432472aac
-  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
   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
-  - 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
@@ -11796,44 +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
-  sha256: 830ca2b0d6c4346846fea969fb66f431bb468b6efdb251b51e89d628b39da84b
-  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
   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
@@ -11843,43 +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
@@ -11892,39 +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
@@ -11939,39 +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
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   - libpng >=1.6.43,<1.7.0a0
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   - numpy >=1.19,<3
   - openjpeg >=2.5.2,<3.0a0
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-  - ucrt >=10.0.20348.0
-  - vc >=14.2,<15
-  - vc14_runtime >=14.29.30139
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   - 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
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-  subdir: osx-64
-  url: https://conda.anaconda.org/conda-forge/osx-64/imagecodecs-2024.6.1-py312h5755a8a_2.conda
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-  md5: 1935447d7fa18910e95ce3e96a8b53e9
+  build: py312h36edd9b_3
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+  url: https://conda.anaconda.org/conda-forge/linux-64/imagecodecs-2024.6.1-py312h36edd9b_3.conda
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-  - 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
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   - libjpeg-turbo >=3.0.0,<4.0a0
   - libjxl >=0.10,<0.11.0a0
   - libpng >=1.6.43,<1.7.0a0
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   - libwebp-base >=1.4.0,<2.0a0
   - libzlib >=1.3.1,<2.0a0
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   - python >=3.12,<3.13.0a0
   - python_abi 3.12.* *_cp312
-  - snappy >=1.2.0,<1.3.0a0
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   - 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
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-  md5: f71c56fe03000e3e2c4e7fa72069eac4
+  build: py312h8902707_3
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+  subdir: osx-64
+  url: https://conda.anaconda.org/conda-forge/osx-64/imagecodecs-2024.6.1-py312h8902707_3.conda
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+  md5: a33ab62781c28ab70e53ed38fc4702e7
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   - bzip2 >=1.0.8,<2.0a0
-  - c-blosc2 >=2.15.0,<2.16.0a0
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   - 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
-  - libdeflate >=1.20,<1.21.0a0
-  - libgcc-ng >=12
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   - libjxl >=0.10,<0.11.0a0
   - libpng >=1.6.43,<1.7.0a0
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   - python >=3.12,<3.13.0a0
   - python_abi 3.12.* *_cp312
-  - snappy >=1.2.0,<1.3.0a0
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   - 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
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-  md5: 1b74007f7eea2706b527c6574d2ddb5b
+  build: py312hd2fe4a3_3
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+  subdir: win-64
+  url: https://conda.anaconda.org/conda-forge/win-64/imagecodecs-2024.6.1-py312hd2fe4a3_3.conda
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+  md5: 67c2d384e46c46d64d4fd6e708071479
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-  - c-blosc2 >=2.15.0,<2.16.0a0
+  - c-blosc2 >=2.15.1,<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
+  - libavif16 >=1.1.1,<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
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   - libjpeg-turbo >=3.0.0,<4.0a0
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-  - python >=3.12,<3.13.0a0 *_cpython
   - python_abi 3.12.* *_cp312
-  - snappy >=1.2.0,<1.3.0a0
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+  - ucrt >=10.0.20348.0
+  - vc >=14.2,<15
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   - 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
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-  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
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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
-  - libdeflate >=1.20,<1.21.0a0
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   - 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
-  - ucrt >=10.0.20348.0
-  - vc >=14.2,<15
-  - vc14_runtime >=14.29.30139
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   - zfp >=1.0.1,<2.0a0
   - zstd >=1.5.6,<1.6.0a0
   license: BSD-3-Clause
   license_family: BSD
-  size: 1590186
-  timestamp: 1719237230813
+  size: 1746737
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 - kind: conda
   name: imageio
   version: 2.34.2
@@ -12215,35 +12716,35 @@ packages:
   timestamp: 1656939625410
 - kind: conda
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   build: pyha770c72_0
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   noarch: python
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-  md5: 3286556cdd99048d198f72c3f6f69103
+  url: https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.2.0-pyha770c72_0.conda
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+  md5: c261d14fc7f49cdd403868998a18c318
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   license: Apache-2.0
   license_family: APACHE
-  size: 27367
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   noarch: generic
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-  md5: 5f8c8ebbe6413a7838cf6ecf14d5d31b
+  url: https://conda.anaconda.org/conda-forge/noarch/importlib_metadata-8.2.0-hd8ed1ab_0.conda
+  sha256: 4a0eacc41786d97176fb53c19d25c4f9b8ab4c9a0ee1fd6f09bc13ca197c21d9
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   license_family: APACHE
-  size: 9511
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   name: importlib_resources
   version: 6.4.0
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   - importlib-metadata
   - packaging
   - python >=3.7
+  constrains:
+  - lazy-loader <0.0a
   license: BSD-3-Clause
   license_family: BSD
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@@ -13551,12 +14054,12 @@ packages:
   timestamp: 1711021498493
 - kind: conda
   name: libavif16
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-  md5: df9815765448800f01aaaa7703203c83
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libavif16-1.1.1-h9a910c9_0.conda
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   license: BSD-2-Clause
   license_family: BSD
-  size: 96634
-  timestamp: 1720732520157
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-  md5: ab39000b12375e3a30ee79fea996e3c5
+  url: https://conda.anaconda.org/conda-forge/linux-64/libavif16-1.1.1-h9b56c87_0.conda
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   license: BSD-2-Clause
   license_family: BSD
-  size: 114634
-  timestamp: 1720732329030
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-  md5: 6fb4d0471456f63e5ebaf05206846034
+  url: https://conda.anaconda.org/conda-forge/osx-64/libavif16-1.1.1-hc1e0c35_0.conda
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   license: BSD-2-Clause
   license_family: BSD
-  size: 108646
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-  md5: d855d66e7eb655db7d6306995b0a3425
+  url: https://conda.anaconda.org/conda-forge/win-64/libavif16-1.1.1-hf4f7b25_0.conda
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   license: BSD-2-Clause
   license_family: BSD
-  size: 97642
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 - kind: conda
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   - blas * openblas
   license: BSD-3-Clause
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   size: 14880
   timestamp: 1721688759937
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   - libcblas 3.9.0 23_osxarm64_openblas
   license: BSD-3-Clause
+  license_family: BSD
   size: 15103
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   license_family: MIT
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   name: lightgbm
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-  build: cpu_py3.10h0fe542f_3
-  build_number: 3
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-  md5: b20f86bb06a0ce508ea591aa0250eeab
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@@ -16316,25 +16774,26 @@ packages:
   - vc >=14.2,<15
   - vc14_runtime >=14.29.30139
   constrains:
+  - pandas >=0.24.0
   - pyarrow >=6.0.1
   - scikit-learn !=0.22.0
-  - pandas >=0.24.0
   - dask >=2.0.0
   - cffi >=1.15.1
   license: MIT
   license_family: MIT
-  size: 1182487
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   name: lightgbm
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   - libgcc-ng >=12
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   - scipy
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+  - pyarrow >=6.0.1
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   - pandas >=0.24.0
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   license: MIT
   license_family: MIT
-  size: 2752950
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-  md5: d83cee5b64e7fd93a8da1331432c3362
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@@ -16374,24 +16833,24 @@ packages:
   - python_abi 3.10.* *_cp310
   - scipy
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-  - dask >=2.0.0
+  - cffi >=1.15.1
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+  - dask >=2.0.0
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-  - cffi >=1.15.1
   license: MIT
   license_family: MIT
-  size: 1602390
-  timestamp: 1719992118764
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   name: lightgbm
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+  version: 4.5.0
+  build: cpu_py3.10he8083a7_1
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   subdir: osx-arm64
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-  md5: 4cce9d1ffc448c86831d311e9648d72a
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-  - scikit-learn !=0.22.0
   - cffi >=1.15.1
-  - dask >=2.0.0
   - pandas >=0.24.0
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+  - scikit-learn !=0.22.0
+  - pyarrow >=6.0.1
   license: MIT
   license_family: MIT
-  size: 1302176
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-  url: https://conda.anaconda.org/conda-forge/linux-64/lightgbm-4.4.0-cpu_py3.11h2f90fb9_3.conda
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+  url: https://conda.anaconda.org/conda-forge/win-64/lightgbm-4.5.0-cpu_py3.11h57d7d38_1.conda
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-  - libgcc-ng >=12
-  - libstdcxx-ng >=12
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-  - ocl-icd >=2.3.2,<3.0a0
   - python >=3.11,<3.12.0a0
   - python_abi 3.11.* *_cp311
   - scipy
+  - ucrt >=10.0.20348.0
+  - vc >=14.2,<15
+  - vc14_runtime >=14.29.30139
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   - scikit-learn !=0.22.0
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   license: MIT
   license_family: MIT
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   - libboost >=1.84.0,<1.85.0a0
+  - libgcc-ng >=12
+  - libstdcxx-ng >=12
   - numpy >=1.17.0
+  - ocl-icd >=2.3.2,<3.0a0
   - python >=3.11,<3.12.0a0
   - python_abi 3.11.* *_cp311
   - scipy
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-  - scikit-learn !=0.22.0
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   - pandas >=0.24.0
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+  - dask >=2.0.0
+  - scikit-learn !=0.22.0
   license: MIT
   license_family: MIT
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   name: lightgbm
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+  version: 4.5.0
+  build: cpu_py3.11h978d779_1
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   subdir: osx-64
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-  md5: 42a41e428b54e63266bc0edada095544
+  url: https://conda.anaconda.org/conda-forge/osx-64/lightgbm-4.5.0-cpu_py3.11h978d779_1.conda
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   - scipy
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-  - cffi >=1.15.1
   - scikit-learn !=0.22.0
-  - pandas >=0.24.0
   - dask >=2.0.0
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+  - pandas >=0.24.0
   license: MIT
   license_family: MIT
-  size: 1663235
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   name: lightgbm
-  version: 4.4.0
-  build: cpu_py3.11hea6280c_3
-  build_number: 3
+  version: 4.5.0
+  build: cpu_py3.11hea6280c_1
+  build_number: 1
   subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/lightgbm-4.4.0-cpu_py3.11hea6280c_3.conda
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-  md5: 2ea38bc86c442988459287fadb93c7db
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/lightgbm-4.5.0-cpu_py3.11hea6280c_1.conda
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@@ -16521,24 +16981,24 @@ packages:
   - python_abi 3.11.* *_cp311
   - scipy
   constrains:
-  - pandas >=0.24.0
+  - dask >=2.0.0
+  - cffi >=1.15.1
   - pyarrow >=6.0.1
   - scikit-learn !=0.22.0
-  - cffi >=1.15.1
-  - dask >=2.0.0
+  - pandas >=0.24.0
   license: MIT
   license_family: MIT
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   name: lightgbm
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+  build: cpu_py3.12h06f1873_1
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   subdir: win-64
-  url: https://conda.anaconda.org/conda-forge/win-64/lightgbm-4.4.0-cpu_py3.12h06f1873_3.conda
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-  md5: c524cdc1ecd8202eb441df270bd48db6
+  url: https://conda.anaconda.org/conda-forge/win-64/lightgbm-4.5.0-cpu_py3.12h06f1873_1.conda
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   - dask >=2.0.0
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-  - pyarrow >=6.0.1
   - pandas >=0.24.0
+  - pyarrow >=6.0.1
+  - cffi >=1.15.1
   license: MIT
   license_family: MIT
-  size: 1233280
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 - kind: conda
   name: lightgbm
-  version: 4.4.0
-  build: cpu_py3.12h1b86e91_3
-  build_number: 3
+  version: 4.5.0
+  build: cpu_py3.12h1b86e91_1
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   subdir: osx-64
-  url: https://conda.anaconda.org/conda-forge/osx-64/lightgbm-4.4.0-cpu_py3.12h1b86e91_3.conda
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-  md5: 70c897b54f2513fbce19c758473ab48c
+  url: https://conda.anaconda.org/conda-forge/osx-64/lightgbm-4.5.0-cpu_py3.12h1b86e91_1.conda
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@@ -16579,25 +17039,26 @@ packages:
   - python_abi 3.12.* *_cp312
   - scipy
   constrains:
+  - scikit-learn !=0.22.0
   - pandas >=0.24.0
   - cffi >=1.15.1
-  - scikit-learn !=0.22.0
-  - dask >=2.0.0
   - pyarrow >=6.0.1
+  - dask >=2.0.0
   license: MIT
   license_family: MIT
-  size: 1650704
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 - kind: conda
   name: lightgbm
-  version: 4.4.0
-  build: cpu_py3.12h1e70e7f_3
-  build_number: 3
+  version: 4.5.0
+  build: cpu_py3.12h2d220ca_1
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   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/lightgbm-4.4.0-cpu_py3.12h1e70e7f_3.conda
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-  md5: c7ebbeb194f8fca89a23ef084c36ac41
+  url: https://conda.anaconda.org/conda-forge/linux-64/lightgbm-4.5.0-cpu_py3.12h2d220ca_1.conda
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   - libgcc-ng >=12
@@ -16608,24 +17069,24 @@ packages:
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   - scipy
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-  - pandas >=0.24.0
   - cffi >=1.15.1
   - dask >=2.0.0
   - scikit-learn !=0.22.0
+  - pyarrow >=6.0.1
+  - pandas >=0.24.0
   license: MIT
   license_family: MIT
-  size: 2803482
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 - kind: conda
   name: lightgbm
-  version: 4.4.0
-  build: cpu_py3.12h6963653_3
-  build_number: 3
+  version: 4.5.0
+  build: cpu_py3.12h6963653_1
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   subdir: osx-arm64
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-  md5: 943130051027e823a1df24f510c0280f
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   constrains:
-  - dask >=2.0.0
-  - scikit-learn !=0.22.0
-  - cffi >=1.15.1
   - pyarrow >=6.0.1
+  - cffi >=1.15.1
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+  build: hcd874cb_1003
+  build_number: 1003
+  subdir: win-64
+  url: https://conda.anaconda.org/conda-forge/win-64/xorg-xextproto-7.3.0-hcd874cb_1003.conda
+  sha256: 04c0a08fd34fa33406c20f729e8f9cc40e8fd898072b952a5c14280fcf26f2e6
+  md5: 6e6c2639620e436bddb7c040cd4f3adb
+  depends:
+  - m2w64-gcc-libs
+  license: MIT
+  license_family: MIT
+  size: 31034
+  timestamp: 1677037259999
 - kind: conda
   name: xorg-xproto
   version: 7.0.31
@@ -28829,6 +29779,21 @@ packages:
   license_family: MIT
   size: 74922
   timestamp: 1607291557628
+- kind: conda
+  name: xorg-xproto
+  version: 7.0.31
+  build: hcd874cb_1007
+  build_number: 1007
+  subdir: win-64
+  url: https://conda.anaconda.org/conda-forge/win-64/xorg-xproto-7.0.31-hcd874cb_1007.tar.bz2
+  sha256: b84cacba8479fa14199c9255fb62e005cacc619e90198c53b1653973709ec331
+  md5: 88f3c65d2ad13826a9e0b162063be023
+  depends:
+  - m2w64-gcc-libs
+  license: MIT
+  license_family: MIT
+  size: 75708
+  timestamp: 1607292254607
 - kind: conda
   name: xz
   version: 5.2.6
@@ -29091,6 +30056,24 @@ packages:
   license_family: MIT
   size: 20917
   timestamp: 1718013395428
+- kind: conda
+  name: zlib
+  version: 1.3.1
+  build: h2466b09_1
+  build_number: 1
+  subdir: win-64
+  url: https://conda.anaconda.org/conda-forge/win-64/zlib-1.3.1-h2466b09_1.conda
+  sha256: 76409556e6c7cb91991cd94d7fc853c9272c2872bd7e3573ff35eb33d6fca5be
+  md5: f8e0a35bf6df768ad87ed7bbbc36ab04
+  depends:
+  - libzlib 1.3.1 h2466b09_1
+  - ucrt >=10.0.20348.0
+  - vc >=14.2,<15
+  - vc14_runtime >=14.29.30139
+  license: Zlib
+  license_family: Other
+  size: 108081
+  timestamp: 1716874767420
 - kind: conda
   name: zlib
   version: 1.3.1
@@ -29204,83 +30187,83 @@ packages:
 - kind: conda
   name: zstandard
   version: 0.23.0
-  build: py312h331e495_0
-  subdir: osx-64
-  url: https://conda.anaconda.org/conda-forge/osx-64/zstandard-0.23.0-py312h331e495_0.conda
-  sha256: c1d379d1062f23e3fbd3dd8548fc6cf61b23d6f96b11e78c4e01f4761580cb02
-  md5: fb62d40e45f51f7d6a7df47c9a12caf4
+  build: py311h4a6b76e_0
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/zstandard-0.23.0-py311h4a6b76e_0.conda
+  sha256: c372898778c58816cf8ad0031504ffb9a451d92c8547e2e524e6e61c1df5d9a3
+  md5: 0571c2ddfd8f08fbf08f3333ac826b2d
   depends:
-  - __osx >=10.13
+  - __osx >=11.0
   - cffi >=1.11
-  - python >=3.12,<3.13.0a0
-  - python_abi 3.12.* *_cp312
+  - python >=3.11,<3.12.0a0
+  - python >=3.11,<3.12.0a0 *_cpython
+  - python_abi 3.11.* *_cp311
   - zstd >=1.5.6,<1.5.7.0a0
   - zstd >=1.5.6,<1.6.0a0
   license: BSD-3-Clause
   license_family: BSD
-  size: 411066
-  timestamp: 1721044218542
+  size: 332378
+  timestamp: 1721044254516
 - kind: conda
   name: zstandard
   version: 0.23.0
-  build: py312h3483029_0
-  subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py312h3483029_0.conda
-  sha256: 7e1e105ea7eab2af591faebf743ff2493f53c313079e316419577925e4492b03
-  md5: eab52e88c858d87cf5a069f79d10bb50
+  build: py311h51fa951_0
+  subdir: osx-64
+  url: https://conda.anaconda.org/conda-forge/osx-64/zstandard-0.23.0-py311h51fa951_0.conda
+  sha256: 5cbac17776b5c8bd27f08f6db4b05a7dc886966370626132654e1418a828931f
+  md5: 9e5d830263cca953b20bb760ca4b6a0d
   depends:
-  - __glibc >=2.17,<3.0.a0
+  - __osx >=10.13
   - cffi >=1.11
-  - libgcc-ng >=12
-  - python >=3.12,<3.13.0a0
-  - python_abi 3.12.* *_cp312
+  - python >=3.11,<3.12.0a0
+  - python_abi 3.11.* *_cp311
   - zstd >=1.5.6,<1.5.7.0a0
   - zstd >=1.5.6,<1.6.0a0
   license: BSD-3-Clause
   license_family: BSD
-  size: 416708
-  timestamp: 1721044154409
+  size: 412173
+  timestamp: 1721044344005
 - kind: conda
   name: zstandard
   version: 0.23.0
-  build: py312h721a963_0
-  subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/zstandard-0.23.0-py312h721a963_0.conda
-  sha256: 6fc0d2f7a0a49a7c1453bb9eacd5456214b6cf000760067d72f0cce464975fa1
-  md5: caf7f5b85615a132c0fa586b82bd59e6
+  build: py311h53056dc_0
+  subdir: win-64
+  url: https://conda.anaconda.org/conda-forge/win-64/zstandard-0.23.0-py311h53056dc_0.conda
+  sha256: 3bd0e287215152d6e3a17090c015368b7632f907cecfb363e7a3391f8ee04f8e
+  md5: a2663051856bfd6ac673a93a0baa7aba
   depends:
-  - __osx >=11.0
   - cffi >=1.11
-  - python >=3.12,<3.13.0a0
-  - python >=3.12,<3.13.0a0 *_cpython
-  - python_abi 3.12.* *_cp312
+  - python >=3.11,<3.12.0a0
+  - python_abi 3.11.* *_cp311
+  - ucrt >=10.0.20348.0
+  - vc >=14.2,<15
+  - vc14_runtime >=14.29.30139
   - zstd >=1.5.6,<1.5.7.0a0
   - zstd >=1.5.6,<1.6.0a0
   license: BSD-3-Clause
   license_family: BSD
-  size: 332489
-  timestamp: 1721044244889
+  size: 322403
+  timestamp: 1721044645946
 - kind: conda
   name: zstandard
   version: 0.23.0
-  build: py312h7606c53_0
-  subdir: win-64
-  url: https://conda.anaconda.org/conda-forge/win-64/zstandard-0.23.0-py312h7606c53_0.conda
-  sha256: 907edf473419a5aff6151900d09bb3f2b2c2ede8964f20ae87cb6fae04d0cbb7
-  md5: c405924e081cb476495ffe72c88e92c2
+  build: py311h5cd10c7_0
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py311h5cd10c7_0.conda
+  sha256: ee4e7202ed6d6027eabb9669252b4dfd8144d4fde644435ebe39ab608086e7af
+  md5: 8efe4fe2396281627b3450af8357b190
   depends:
+  - __glibc >=2.17,<3.0.a0
   - cffi >=1.11
-  - python >=3.12,<3.13.0a0
-  - python_abi 3.12.* *_cp312
-  - ucrt >=10.0.20348.0
-  - vc >=14.2,<15
-  - vc14_runtime >=14.29.30139
+  - libgcc-ng >=12
+  - python >=3.11,<3.12.0a0
+  - python_abi 3.11.* *_cp311
   - zstd >=1.5.6,<1.5.7.0a0
   - zstd >=1.5.6,<1.6.0a0
   license: BSD-3-Clause
   license_family: BSD
-  size: 320649
-  timestamp: 1721044547910
+  size: 416323
+  timestamp: 1721044178290
 - kind: conda
   name: zstd
   version: 1.5.6
diff --git a/pixi.toml b/pixi.toml
index d9d84d7c..ff5a9750 100644
--- a/pixi.toml
+++ b/pixi.toml
@@ -63,6 +63,9 @@ sphinx_rtd_theme = "*"
 sphinxcontrib-apidoc = "*"
 sphinx-autodoc-typehints = "*"
 myst-nb = "*"
+econml = ">=0.15"
+doubleml = "*"
+statsmodels = "*"
 
 [feature.docs.tasks]
 docs = "cd docs && make html "
diff --git a/tests/test_drlearner.py b/tests/test_drlearner.py
index 75790327..14298e21 100644
--- a/tests/test_drlearner.py
+++ b/tests/test_drlearner.py
@@ -133,3 +133,22 @@ def test_drlearner_onnx(
         {"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,
+):
+    X, _, W, Y, _, tau = (
+        numerical_experiment_dataset_continuous_outcome_binary_treatment_linear_te
+    )
+    ml = DRLearner(
+        False,
+        2,
+        LinearRegression,
+        LinearRegression,
+        LogisticRegression,
+        n_folds=2,
+    )
+    ml.fit_all_nuisance(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)