Counterfactual Representation Learning with Balancing Weights

Serge Assaad, Shuxi Zeng, Chenyang Tao, Shounak Datta, Nikhil Mehta, Ricardo Henao, Fan Li, Lawrence Carin
Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, PMLR 130:1972-1980, 2021.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v130-assaad21a, title = { Counterfactual Representation Learning with Balancing Weights }, author = {Assaad, Serge and Zeng, Shuxi and Tao, Chenyang and Datta, Shounak and Mehta, Nikhil and Henao, Ricardo and Li, Fan and Carin Duke, Lawrence}, booktitle = {Proceedings of The 24th International Conference on Artificial Intelligence and Statistics}, pages = {1972--1980}, year = {2021}, editor = {Banerjee, Arindam and Fukumizu, Kenji}, volume = {130}, series = {Proceedings of Machine Learning Research}, month = {13--15 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v130/assaad21a/assaad21a.pdf}, url = {https://proceedings.mlr.press/v130/assaad21a.html}, 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. } }
Endnote
%0 Conference Paper %T Counterfactual Representation Learning with Balancing Weights %A Serge Assaad %A Shuxi Zeng %A Chenyang Tao %A Shounak Datta %A Nikhil Mehta %A Ricardo Henao %A Fan Li %A Lawrence Carin %B Proceedings of The 24th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2021 %E Arindam Banerjee %E Kenji Fukumizu %F pmlr-v130-assaad21a %I PMLR %P 1972--1980 %U https://proceedings.mlr.press/v130/assaad21a.html %V 130 %X 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.
APA
Assaad, S., Zeng, S., Tao, C., Datta, S., Mehta, N., Henao, R., Li, F. & Carin, L.. (2021). Counterfactual Representation Learning with Balancing Weights . Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 130:1972-1980 Available from https://proceedings.mlr.press/v130/assaad21a.html.

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