Counterfactual Representation Learning with Balancing Weights

Serge Assaad, Shuxi Zeng, Nikhil Mehta, Ricardo Henao, Chenyang Tao, Shounak Datta, Fan Li, Lawrence Carin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

20 Scopus citations

Abstract

A key to causal inference with observational data is achieving balance in predictive features associated with each treatment type. Recent literature has explored representation learning to achieve this goal. In this work, we discuss the pitfalls of these strategies - such as a steep trade-off between achieving balance and predictive power - and present a remedy via the integration of balancing weights in causal learning. Specifically, we theoretically link balance to the quality of propensity estimation, emphasize the importance of identifying a proper target population, and elaborate on the complementary roles of feature balancing and weight adjustments. Using these concepts, we then develop an algorithm for flexible, scalable and accurate estimation of causal effects. Finally, we show how the learned weighted representations may serve to facilitate alternative causal learning procedures with appealing statistical features. We conduct an extensive set of experiments on both synthetic examples and standard benchmarks, and report encouraging results relative to state-of-the-art baselines.
Original languageEnglish (US)
Title of host publicationProceedings of Machine Learning Research
PublisherML Research Press
Pages1972-1980
Number of pages9
StatePublished - Jan 1 2021
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2023-09-25

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