A Generative Approach for Treatment Effect Estimation under Collider Bias: From an Out-of-Distribution Perspective

Baohong Li, Haoxuan Li, Anpeng Wu, Minqin Zhu, Shiyuan Peng, Qingyu Cao, Kun Kuang
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:28132-28145, 2024.

Abstract

Resulting from non-random sample selection caused by both the treatment and outcome, collider bias poses a unique challenge to treatment effect estimation using observational data whose distribution differs from that of the target population. In this paper, we rethink collider bias from an out-of-distribution (OOD) perspective, considering that the entire data space of the target population consists of two different environments: The observational data selected from the target population belongs to a seen environment labeled with $S=1$ and the missing unselected data belongs to another unseen environment labeled with $S=0$. Based on this OOD formulation, we utilize small-scale representative data from the entire data space with no environmental labels and propose a novel method, i.e., Coupled Counterfactual Generative Adversarial Model (C$^2$GAM), to simultaneously generate the missing $S=0$ samples in observational data and the missing $S$ labels in the small-scale representative data. With the help of C$^2$GAM, collider bias can be addressed by combining the generated $S=0$ samples and the observational data to estimate treatment effects. Extensive experiments on synthetic and real-world data demonstrate that plugging C$^2$GAM into existing treatment effect estimators achieves significant performance improvements.

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-li24al, title = {A Generative Approach for Treatment Effect Estimation under Collider Bias: From an Out-of-Distribution Perspective}, author = {Li, Baohong and Li, Haoxuan and Wu, Anpeng and Zhu, Minqin and Peng, Shiyuan and Cao, Qingyu and Kuang, Kun}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {28132--28145}, year = {2024}, editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix}, volume = {235}, series = {Proceedings of Machine Learning Research}, month = {21--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/li24al/li24al.pdf}, url = {https://proceedings.mlr.press/v235/li24al.html}, abstract = {Resulting from non-random sample selection caused by both the treatment and outcome, collider bias poses a unique challenge to treatment effect estimation using observational data whose distribution differs from that of the target population. In this paper, we rethink collider bias from an out-of-distribution (OOD) perspective, considering that the entire data space of the target population consists of two different environments: The observational data selected from the target population belongs to a seen environment labeled with $S=1$ and the missing unselected data belongs to another unseen environment labeled with $S=0$. Based on this OOD formulation, we utilize small-scale representative data from the entire data space with no environmental labels and propose a novel method, i.e., Coupled Counterfactual Generative Adversarial Model (C$^2$GAM), to simultaneously generate the missing $S=0$ samples in observational data and the missing $S$ labels in the small-scale representative data. With the help of C$^2$GAM, collider bias can be addressed by combining the generated $S=0$ samples and the observational data to estimate treatment effects. Extensive experiments on synthetic and real-world data demonstrate that plugging C$^2$GAM into existing treatment effect estimators achieves significant performance improvements.} }
Endnote
%0 Conference Paper %T A Generative Approach for Treatment Effect Estimation under Collider Bias: From an Out-of-Distribution Perspective %A Baohong Li %A Haoxuan Li %A Anpeng Wu %A Minqin Zhu %A Shiyuan Peng %A Qingyu Cao %A Kun Kuang %B Proceedings of the 41st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Ruslan Salakhutdinov %E Zico Kolter %E Katherine Heller %E Adrian Weller %E Nuria Oliver %E Jonathan Scarlett %E Felix Berkenkamp %F pmlr-v235-li24al %I PMLR %P 28132--28145 %U https://proceedings.mlr.press/v235/li24al.html %V 235 %X Resulting from non-random sample selection caused by both the treatment and outcome, collider bias poses a unique challenge to treatment effect estimation using observational data whose distribution differs from that of the target population. In this paper, we rethink collider bias from an out-of-distribution (OOD) perspective, considering that the entire data space of the target population consists of two different environments: The observational data selected from the target population belongs to a seen environment labeled with $S=1$ and the missing unselected data belongs to another unseen environment labeled with $S=0$. Based on this OOD formulation, we utilize small-scale representative data from the entire data space with no environmental labels and propose a novel method, i.e., Coupled Counterfactual Generative Adversarial Model (C$^2$GAM), to simultaneously generate the missing $S=0$ samples in observational data and the missing $S$ labels in the small-scale representative data. With the help of C$^2$GAM, collider bias can be addressed by combining the generated $S=0$ samples and the observational data to estimate treatment effects. Extensive experiments on synthetic and real-world data demonstrate that plugging C$^2$GAM into existing treatment effect estimators achieves significant performance improvements.
APA
Li, B., Li, H., Wu, A., Zhu, M., Peng, S., Cao, Q. & Kuang, K.. (2024). A Generative Approach for Treatment Effect Estimation under Collider Bias: From an Out-of-Distribution Perspective. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:28132-28145 Available from https://proceedings.mlr.press/v235/li24al.html.

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