To help you get started, we've collected the most common ways that causalml is being used within popular public projects.

uber / causalml / tests / test_meta_learners.py View on Github

random_state=RANDOM_SEED)

    uplift_model = BaseTClassifier(learner=LogisticRegression())

    uplift_model.fit(X=df_train[x_names].values,
                     treatment=df_train['treatment_group_key'].values,
                     y=df_train[CONVERSION].values)

    y_pred = uplift_model.predict(X=df_test[x_names].values,
                                  treatment=df_test['treatment_group_key'].values)

    auuc_metrics = pd.DataFrame(
        np.c_[y_pred, df_test['treatment_group_key'].values, df_test[CONVERSION].values],
        columns=['y_pred', 'W', CONVERSION])

    cumgain = get_cumgain(auuc_metrics,
                          outcome_col=CONVERSION,
                          treatment_col='W')

    # Check if the cumulative gain when using the model's prediction is
    # higher than it would be under random targeting
    assert cumgain['y_pred'].sum() > cumgain['Random'].sum()

View more ways to use causalml

Disclaimer

This project is stable and being incubated for long-term support. It may contain new experimental code, for which APIs are subject to change.

Causal ML: A Python Package for Uplift Modeling and Causal Inference with ML

Causal ML is a Python package that provides a suite of uplift modeling and causal inference methods using machine learning algorithms based on recent research [1]. It provides a standard interface that allows user to estimate the Conditional Average Treatment Effect (CATE) or Individual Treatment Effect (ITE) from experimental or observational data. Essentially, it estimates the causal impact of intervention T on outcome Y for users with observed features X, without strong assumptions on the model form. Typical use cases include

• Campaign targeting optimization: An important lever to increase ROI in an advertising campaign is to target the ad to the set of customers who will have a favorable response in a given KPI such as engagement or sales. CATE identifies these customers by estimating the effect of the KPI from ad exposure at the individual level from A/B experiment or historical observational data.

• Personalized engagement: A company has multiple options to interact with its customers such as different product choices in up-sell or messaging channels for communications. One can use CATE to estimate the heterogeneous treatment effect for each customer and treatment option combination for an optimal personalized recommendation system.

Documentation

Documentation is available at:

https://causalml.readthedocs.io/en/latest/about.html

Installation

Installation instructions are available at:

https://causalml.readthedocs.io/en/latest/installation.html

Quickstart

Quickstarts with code-snippets are available at:

https://causalml.readthedocs.io/en/latest/quickstart.html

Example Notebooks

Example notebooks are available at:

https://causalml.readthedocs.io/en/latest/examples.html

Contributing

We welcome community contributors to the project. Before you start, please read our code of conduct and check out contributing guidelines first.

Versioning

We document versions and changes in our changelog.

License

This project is licensed under the Apache 2.0 License - see the LICENSE file for details.

References

Documentation

• Causal ML API documentation

Conference Talks and Publications by CausalML Team

• (Talk) Introduction to CausalML at Causal Data Science Meeting 2021
• (Talk) Introduction to CausalML at 2021 Conference on Digital Experimentation @ MIT (CODE@MIT)
• (Talk) Causal Inference and Machine Learning in Practice with EconML and CausalML: Industrial Use Cases at Microsoft, TripAdvisor, Uber at KDD 2021 Tutorials (website and slide links)
• (Publication) CausalML White Paper Causalml: Python package for causal machine learning
• (Publication) Uplift Modeling for Multiple Treatments with Cost Optimization at 2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA)
• (Publication) Feature Selection Methods for Uplift Modeling

Citation

To cite CausalML in publications, you can refer to the following sources:

Whitepaper: CausalML: Python Package for Causal Machine Learning

Bibtex: > @misc{chen2020causalml, > title={CausalML: Python Package for Causal Machine Learning}, > author={Huigang Chen and Totte Harinen and Jeong-Yoon Lee and Mike Yung and Zhenyu Zhao}, > year={2020}, > eprint={2002.11631}, > archivePrefix={arXiv}, > primaryClass={cs.CY} >}

Literature

1. Chen, Huigang, Totte Harinen, Jeong-Yoon Lee, Mike Yung, and Zhenyu Zhao. "Causalml: Python package for causal machine learning." arXiv preprint arXiv:2002.11631 (2020).
2. Radcliffe, Nicholas J., and Patrick D. Surry. "Real-world uplift modelling with significance-based uplift trees." White Paper TR-2011-1, Stochastic Solutions (2011): 1-33.
3. Zhao, Yan, Xiao Fang, and David Simchi-Levi. "Uplift modeling with multiple treatments and general response types." Proceedings of the 2017 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2017.
4. Hansotia, Behram, and Brad Rukstales. "Incremental value modeling." Journal of Interactive Marketing 16.3 (2002): 35-46.
5. Jannik Rößler, Richard Guse, and Detlef Schoder. "The Best of Two Worlds: Using Recent Advances from Uplift Modeling and Heterogeneous Treatment Effects to Optimize Targeting Policies". International Conference on Information Systems (2022)
6. Su, Xiaogang, et al. "Subgroup analysis via recursive partitioning." Journal of Machine Learning Research 10.2 (2009).
7. Su, Xiaogang, et al. "Facilitating score and causal inference trees for large observational studies." Journal of Machine Learning Research 13 (2012): 2955.
8. Athey, Susan, and Guido Imbens. "Recursive partitioning for heterogeneous causal effects." Proceedings of the National Academy of Sciences 113.27 (2016): 7353-7360.
9. Künzel, Sören R., et al. "Metalearners for estimating heterogeneous treatment effects using machine learning." Proceedings of the national academy of sciences 116.10 (2019): 4156-4165.
10. Nie, Xinkun, and Stefan Wager. "Quasi-oracle estimation of heterogeneous treatment effects." arXiv preprint arXiv:1712.04912 (2017).
11. Bang, Heejung, and James M. Robins. "Doubly robust estimation in missing data and causal inference models." Biometrics 61.4 (2005): 962-973.
12. Van Der Laan, Mark J., and Daniel Rubin. "Targeted maximum likelihood learning." The international journal of biostatistics 2.1 (2006).
13. Kennedy, Edward H. "Optimal doubly robust estimation of heterogeneous causal effects." arXiv preprint arXiv:2004.14497 (2020).
14. Louizos, Christos, et al. "Causal effect inference with deep latent-variable models." arXiv preprint arXiv:1705.08821 (2017).
15. Shi, Claudia, David M. Blei, and Victor Veitch. "Adapting neural networks for the estimation of treatment effects." 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), 2019.
16. Zhao, Zhenyu, Yumin Zhang, Totte Harinen, and Mike Yung. "Feature Selection Methods for Uplift Modeling." arXiv preprint arXiv:2005.03447 (2020).
17. Zhao, Zhenyu, and Totte Harinen. "Uplift modeling for multiple treatments with cost optimization." In 2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 422-431. IEEE, 2019.

Related projects

• uplift: uplift models in R
• grf: generalized random forests that include heterogeneous treatment effect estimation in R
• rlearner: A R package that implements R-Learner
• DoWhy: Causal inference in Python based on Judea Pearl's do-calculus
• EconML: A Python package that implements heterogeneous treatment effect estimators from econometrics and machine learning methods