Merge branch 'main' into onnx

README.md

@@ -6,7 +6,41 @@
 [![PypiVersion](https://img.shields.io/pypi/v/metalearners.svg?logo=pypi&logoColor=white)](https://pypi.org/project/metalearners)
 [![codecov.io](https://codecov.io/github/QuantCo/metalearners/coverage.svg?branch=main)](https://codecov.io/github/QuantCo/metalearners?branch=main)
 
-MetaLearners for CATE estimation
+MetaLearners for Conditional Average Treatment Effect (CATE) estimation
+
+The library focuses on providing
+
+- Methodologically sound cross-fitting
+- Convenient access to and reuse of base models
+- Consistent APIs across Metalearners
+- Support for more than binary treatment variants
+- Integrations with `pandas`, `shap`, `lime`, `optuna` and soon `onnx`
+
+## Example
+
+```python
+
+df = ...
+
+from metalearners import RLearner
+from lightgbm import LGBMClassifier, LGBMRegressor
+
+rlearner = RLearner(
+    nuisance_model_factory=LGBMRegressor,
+    propensity_model_factory=LGBMClassifier,
+    treatment_model_factory=LGBMRegressor,
+    is_classification=False,
+    n_variants=2,
+)
+
+features = ["age", "weight", "height"]
+rlearner.fit(df[features], df["treatment"], df["outcomes"])
+cate_estimates = rlearner.predict(df[features], is_oos=False)
+```
+
+Please refer to our
+[docs](https://metalearners.readthedocs.io/en/latest/?badge=latest)
+for many more in-depth and reproducible examples.
 
 ## Installation