References

Tutorials and blog posts

• Retrospective Estimation (blog post)

• Uplift modeling tutorial at WebConf’2021

• Personalization in Practice tutorial at WSDM’2021

Related packages

• CausalML: a Python package for uplift modeling and causal inference with machine learning

• EconML: a Python package for estimating heterogeneous treatment effects from observational data via machine learning

Papers

1

Susan Athey and Guido W Imbens. Machine learning methods for estimating heterogeneous causal effects. stat, 1050(5):1–26, 2015.

2

Shuyang Du, James Lee, and Farzin Ghaffarizadeh. Improve user retention with causal learning. In The 2019 ACM SIGKDD Workshop on Causal Discovery, 34–49. PMLR, 2019.

3

Dmitri Goldenberg, Javier Albert, Lucas Bernardi, and Pablo Estevez. Free lunch! retrospective uplift modeling for dynamic promotions recommendation within roi constraints. In Fourteenth ACM Conference on Recommender Systems, 486–491. 2020.

4

Maciej Jaskowski and Szymon Jaroszewicz. Uplift modeling for clinical trial data. In ICML Workshop on Clinical Data Analysis, volume 46. 2012.

5

Sören R Künzel, Jasjeet S Sekhon, Peter J Bickel, and Bin Yu. Metalearners for estimating heterogeneous treatment effects using machine learning. Proceedings of the national academy of sciences, 116(10):4156–4165, 2019.

6

Xinkun Nie and Stefan Wager. Quasi-oracle estimation of heterogeneous treatment effects. Biometrika, 108(2):299–319, 2021.

7

Nicholas J Radcliffe and Patrick D Surry. Real-world uplift modelling with significance-based uplift trees. White Paper TR-2011-1, Stochastic Solutions, pages 1–33, 2011.

8

Hao Sun, Shuyang Du, and Stefan Wager. Treatment allocation under uncertain costs. arXiv preprint arXiv:2103.11066, 2021.