metalearners
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,optunaand soononnx
Example
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["outcomes"], df["treatment"])
cate_estimates = rlearner.predict(df[features], is_oos=False)
Please refer to our docs for many more in-depth and reproducible examples.
Installation
metalearners can either be installed via PyPI with
$ pip install metalearners
or via conda-forge with
$ conda install metalearners -c conda-forge
Development
Development instructions can be found here.
1 maintainer
Metadata
1 maintainer
Release files for metalearners 0.13.0
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