causeinfer is a Python package for estimating average and conditional average treatment effects using machine learning. The goal is to compile causal inference models both standard and advanced, as well as demonstrate their usage and efficacy - all this with the overarching ambition to help people learn causal inference techniques across business, medical, and socioeconomic fields. See the documentation for a full outline of the package including the available models and datasets.
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causeinfer can be downloaded from PyPI via pip or sourced directly from this repository:
pip install causeinfer
git clone https://github.com/andrewtavis/causeinfer.git
cd causeinfer
python setup.py install
import causeinfer
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An interaction term between treatment and covariates is added to the data to allow for a basic single model application (Lo, 2002).
Units are categorized into two or four classes to derive treatment effects from favorable class attributes (Lai, 2006; Kane, et al, 2014; Shaar, et al, 2016).
Weighted versions of the binary class transformation approach that are meant to dampen the original model's inherently noisy results (Shaar, et al, 2016). ```python # Reflective Uplift Transformation from causeinfer.standard_algorithms.reflective import ReflectiveUplift from sklearn.ensemble import RandomForestClassifier ru = ReflectiveUplift(model=RandomForestClassifier(**kwargs)) ru.fit(X=X_train, y=y_train, w=w_train) # An array of predicted probabilities (P(Favorable Class), P(Unfavorable Class)) ru_probas = ru.predict_proba(X=X_test) ``` ```python # Pessimistic Uplift Transformation from causeinfer.standard_algorithms.pessimistic import PessimisticUplift from sklearn.ensemble import RandomForestClassifier pu = PessimisticUplift(model=RandomForestClassifier(**kwargs)) pu.fit(X=X_train, y=y_train, w=w_train) # An array of predicted probabilities (P(Favorable Class), P(Unfavorable Class)) pu_probas = pu.predict_proba(X=X_test) ```
- Under consideration for inclusion in causeinfer: - Generalized Random Forest via the R/C++ [grf](https://github.com/grf-labs/grf) - Athey, Tibshirani, and Wager (2019) - The X-Learner - Kunzel, et al (2019) - The R-Learner - Nie and Wager (2017) - Double Machine Learning - Chernozhukov, et al (2018) - Information Theory Trees/Forests - Soltys, et al (2015)
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Comparisons across stratified, ordered treatment response groups are used to derive model efficiency. ```python from causeinfer.evaluation import plot_cum_gain, plot_qini visual_eval_dict = { "y_test": y_test, "w_test": w_test, "two_model": tm_effects, "interaction_term": it_effects, "binary_trans": bt_effects, "quaternary_trans": qt_effects, } df_visual_eval = pd.DataFrame(visual_eval_dict, columns=visual_eval_dict.keys()) model_pred_cols = [ col for col in visual_eval_dict.keys() if col not in ["y_test", "w_test"] ] ``` ```python fig, (ax1, ax2) = plt.subplots(ncols=2, sharey=False, figsize=(20, 5)) plot_cum_effect( df=df_visual_eval, n=100, models=models, percent_of_pop=True, outcome_col="y_test", treatment_col="w_test", normalize=True, random_seed=42, axis=ax1, legend_metrics=True, ) plot_qini( # or plot_cum_gain df=df_visual_eval, n=100, models=models, percent_of_pop=True, outcome_col="y_test", treatment_col="w_test", normalize=True, random_seed=42, axis=ax2, legend_metrics=True, ) ``` Hillstrom Metrics
Easily iterate models to derive their average effects and prediction variances. See a full example across all datasets and models in [examples/model_iteration](https://github.com/andrewtavis/causeinfer/blob/main/examples/model_iteration.ipynb), with the results being shown below: | | TwoModel | InteractionTerm | BinaryTransformation | QuaternaryTransformation | ReflectiveUplift | PessimisticUplift | | :--------------- | :--------------------- | :-------------------- | :--------------------- | :----------------------- | :----------------------- | :----------------------- | | Hillstrom | -5.4762 ± 13.589\*\*\* | -5.047 ± 15.417\*\*\* | 0.5178 ± 15.7252\*\*\* | 0.7397 ± 14.7509\*\*\* | 4.4872 ± 18.5918\*\*\*\* | -6.0052 ± 17.936\*\*\*\* | | Mayo PBC | -0.145 ± 0.29 | -0.1335 ± 0.4471 | 0.5542 ± 0.4268 | 0.5315 ± 0.4424 | -0.8774 ± 0.233 | 0.1392 ± 0.3587 | | CMF Microfinance | 18.7289 ± 5.9138\*\* | 17.0616 ± 6.6993\*\* | nan | nan | nan | nan |
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- [Hillstrom Email Marketing](https://blog.minethatdata.com/2008/03/minethatdata-e-mail-analytics-and-data.html) - Is directly downloaded and formatted with causeinfer (see [causeinfer.data.hillstrom](https://github.com/andrewtavis/causeinfer/blob/main/src/causeinfer/data/hillstrom.py)) - How to use this dataset is shown in [examples/business_hillstrom](https://github.com/andrewtavis/causeinfer/blob/main/examples/business_hillstrom.ipynb) and below ```python from causeinfer.data import hillstrom hillstrom.download_hillstrom() data_hillstrom = hillstrom.load_hillstrom( user_file_path="datasets/hillstrom.csv", format_covariates=True, normalize=True ) df = pd.DataFrame( data_hillstrom["dataset_full"], columns=data_hillstrom["dataset_full_names"] ) ``` - [Criteo Uplift](https://ailab.criteo.com/criteo-uplift-prediction-dataset/) - Needed [(see issue)](https://github.com/andrewtavis/causeinfer/issues/18): - Download and formatting script - Example notebook - Tests - Documentation
- [Mayo Clinic PBC](https://www.mayo.edu/research/documents/pbchtml/DOC-10027635) - Is directly downloaded and formatted with causeinfer (see [causeinfer.data.mayo_pbc](https://github.com/andrewtavis/causeinfer/blob/main/src/causeinfer/data/mayo_pbc.py)) - Also included in the [datasets directory](https://github.com/andrewtavis/causeinfer/blob/main/src/causeinfer/data/datasets) for direct download - How to use this dataset is shown in [examples/medical_mayo_pbc](https://github.com/andrewtavis/causeinfer/blob/main/examples/medical_mayo_pbc.ipynb) and below ```python from causeinfer.data import mayo_pbc mayo_pbc.download_mayo_pbc() data_mayo_pbc = mayo_pbc.load_mayo_pbc( user_file_path="datasets/mayo_pbc.text", format_covariates=True, normalize=True ) df = pd.DataFrame( data_mayo_pbc["dataset_full"], columns=data_mayo_pbc["dataset_full_names"] ) ``` - [Pintilie Tamoxifen](https://onlinelibrary.wiley.com/doi/book/10.1002/9780470870709) - Accompanied the linked text, but is now unavailable, so it is included in the [datasets directory](https://github.com/andrewtavis/causeinfer/blob/main/src/causeinfer/data/datasets) for direct download - Needed [(see issue)](https://github.com/andrewtavis/causeinfer/issues/19): - Formatting script - Example notebook - Tests - Documentation
- [CMF Microfinance](https://www.aeaweb.org/articles?id=10.1257/app.20130533) - Accompanied the linked text, but is now unavailable. It is included in the [datasets directory](https://github.com/andrewtavis/causeinfer/blob/main/src/causeinfer/data/datasets) for direct download - Is formatted with causeinfer (see [causeinfer.data.cmf_micro](https://github.com/andrewtavis/causeinfer/blob/main/src/causeinfer/data/cmf_micro.py)) - How to use this dataset is shown in [examples/socioeconomic_cmf_micro](https://github.com/andrewtavis/causeinfer/blob/main/examples/socioeconomic_cmf_micro.ipynb) and below ```python from causeinfer.data import cmf_micro data_cmf_micro = cmf_micro.load_cmf_micro( user_file_path="datasets/cmf_micro", format_covariates=True, normalize=True ) df = pd.DataFrame( data_cmf_micro["dataset_full"], columns=data_cmf_micro["dataset_full_names"] ) ``` - [Lalonde Job Training](https://users.nber.org/~rdehejia/data/.nswdata2.html) - Needed [(see issue)](https://github.com/andrewtavis/causeinfer/issues/20): - Download and formatting script - Example notebook - Tests - Documentation
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Please see the contribution guidelines if you are interested in contributing to this project. Work that is in progress or could be implemented includes:
Adding more baseline models and datasets (see issues)
Converting GRF files to Python and connecting them to the C++ boiler plate
Adding a data simulator (see issue)
Finding more causal inference datasets to be added (see issue)
Adding a predict
method to binary_transformation and quaternary_transformation
Updating and refining the documentation
Improving tests for greater code coverage
Improving code quality by refactoring large functions and checking conventions
Python - https://github.com/uber/causalml - https://github.com/Minyus/causallift - https://github.com/maks-sh/scikit-uplift - https://github.com/duketemon/pyuplift - https://github.com/microsoft/EconML - https://github.com/Microsoft/dowhy - https://github.com/wayfair/pylift/ - https://github.com/jszymon/uplift_sklearn Other Languages - https://github.com/grf-labs/grf (R/C++) - [https://github.com/soerenkuenzel/causalToolbox/X-Learner](https://github.com/soerenkuenzel/causalToolbox/blob/a06d81d74f4d575a8b34dc6b718db2778cfa0be9/R/XRF.R) (R/C++) - https://github.com/xnie/rlearner (R) Data and Misc - https://github.com/rguo12/awesome-causality-data - https://github.com/rguo12/awesome-causality-algorithms - https://github.com/zhaoxiliang/causalinference
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- [pyuplift](https://github.com/duketemon/pyuplift) by [duketemon](https://github.com/duketemon) ([License](https://github.com/duketemon/pyuplift/blob/master/LICENSE)) - [Causal ML](https://github.com/uber/causalml) by [Uber](https://github.com/uber) ([License](https://github.com/uber/causalml/blob/master/LICENSE))
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