CausalML: Python Package for Causal Machine Learning

P. Rzepakowski and S. Jaroszewicz, “Decision trees for uplift modeling with single and multiple treatments,” Knowl. Inf. Syst. , vol. 32, no. 2, pp. 303–327, Aug. 2012

2012

Earlier work this paper cites.

L. Zaniewicz and S. Jaroszewicz, “Support vector machines for uplift modeling,” in 2013 IEEE 13th International Conference on Data Mining Workshops , Dec. 2013, pp. 131–138

2013

Earlier work this paper cites.

L. Guelman, M. Guillén, and A. M. Pérez-Marín, “Uplift random forests,” Cybern. Syst. , vol. 46, no. 3-4, pp. 230–248, May 2015

2015

Earlier work this paper cites.

M. Sołtys, S. Jaroszewicz, and P. Rzepakowski, “Ensemble methods for uplift modeling,” Data Min. Knowl. Discov. , vol. 29, no. 6, pp. 1531–1559, Nov. 2015

2015

Earlier work this paper cites.

S. Wager and S. Athey, “Estimation and inference of heterogeneous treatment effects using random forests,” Oct. 2015

2015

Cited alongside, same era.

P. Gutierrez and J.-Y. Gerardy, “Causal inference and uplift modeling a review of the literature,” JMLR: Workshop and Conference Proceedings 67 , 2016

2016

Cited alongside, same era.

J. Grimmer, S. Messing, and S. J. Westwood, “Estimating heterogeneous treatment effects and the effects of heterogeneous treatments with ensemble methods,” Polit. Anal. , vol. 25, no. 4, pp. 413–434, Oct. 2017

2017

Cited alongside, same era.

S. R. Künzel, J. S. Sekhon, P. J. Bickel, and B. Yu, “Meta-learners for estimating heterogeneous treatment effects using machine learning,” Jun. 2017

2017

Later among the works it cites.

Y. Zhao, X. Fang, and D. Simchi-Levi, “Uplift modeling with multiple treatments and general response types,” May 2017

2017

Later among the works it cites.

X. Nie and S. Wager, “Quasi-oracle estimation of heterogeneous treatment effects,” arXiv preprint arXiv:1712.04912 , 2017

Original

2017

Later among the works it cites.