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Causal ML：用于提升建模和因果推断的Python包

Causal ML是一个Python包，它提供了一套基于最新研究的使用机器学习算法进行提升建模和因果推断的方法[1]。它提供了一个标准接口，允许用户从实验数据或观察数据中估计条件平均处理效应（CATE）或个体处理效应（ITE）。本质上，它估计干预T对具有观察特征X的用户的结果Y的因果影响，而不对模型形式做出强假设。典型的使用场景包括

营销活动目标优化：提高广告活动投资回报率的一个重要杠杆是将广告定向投放给在特定KPI（如参与度或销售额）方面会有积极响应的客户群。CATE通过从A/B实验或历史观察数据中估计个体层面的广告曝光对KPI的影响来识别这些客户。
个性化互动：公司有多种与客户互动的选择，如追加销售的不同产品选择或沟通的消息渠道。可以使用CATE来估计每个客户和处理选项组合的异质处理效应，从而建立最佳的个性化推荐系统。

文档

文档可在以下地址获取：

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

安装

安装说明可在以下地址获取：

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

快速入门

包含代码片段的快速入门指南可在以下地址获取：

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

示例笔记本

示例笔记本可在以下地址获取：

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

贡献

我们欢迎社区贡献者参与项目。在开始之前，请阅读我们的行为准则并查看贡献指南。

版本控制

我们在更新日志中记录版本和变更。

许可证

本项目采用Apache 2.0许可证 - 详情请参阅LICENSE文件。

参考文献

文档

Causal ML API文档

CausalML团队的会议演讲和出版物

（演讲）在2021年因果数据科学会议上介绍CausalML
（演讲）在2021年麻省理工学院数字实验会议（CODE@MIT）上介绍CausalML
（演讲）在KDD 2021教程上介绍微软、TripAdvisor和Uber的EconML和CausalML实践中的因果推断和机器学习（网站和幻灯片链接）
（出版物）CausalML白皮书Causalml: Python package for causal machine learning
（出版物）Uplift Modeling for Multiple Treatments with Cost Optimization发表于2019 IEEE国际数据科学与高级分析会议（DSAA）
（出版物）Feature Selection Methods for Uplift Modeling

引用

要在出版物中引用CausalML，您可以参考以下来源：

白皮书： 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} }

文献

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).
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.
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.
Hansotia, Behram, and Brad Rukstales. "Incremental value modeling." Journal of Interactive Marketing 16.3 (2002): 35-46.
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)
Su, Xiaogang, et al. "Subgroup analysis via recursive partitioning." Journal of Machine Learning Research 10.2 (2009).
Su, Xiaogang, et al. "Facilitating score and causal inference trees for large observational studies." Journal of Machine Learning Research 13 (2012): 2955.
Athey, Susan, and Guido Imbens. "Recursive partitioning for heterogeneous causal effects." Proceedings of the National Academy of Sciences 113.27 (2016): 7353-7360.
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.
Nie, Xinkun, and Stefan Wager. "Quasi-oracle estimation of heterogeneous treatment effects." arXiv preprint arXiv:1712.04912 (2017).
Bang, Heejung, and James M. Robins. "Doubly robust estimation in missing data and causal inference models." Biometrics 61.4 (2005): 962-973.
Van Der Laan, Mark J., and Daniel Rubin. "Targeted maximum likelihood learning." The international journal of biostatistics 2.1 (2006).
Kennedy, Edward H. "Optimal doubly robust estimation of heterogeneous causal effects." arXiv preprint arXiv:2004.14497 (2020).
Louizos, Christos, et al. "Causal effect inference with deep latent-variable models." arXiv preprint arXiv:1705.08821 (2017).
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.
Zhao, Zhenyu, Yumin Zhang, Totte Harinen, and Mike Yung. "Feature Selection Methods for Uplift Modeling." arXiv preprint arXiv:2005.03447 (2020).
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.