PairNet: Training with Observed Pairs to Estimate Individual Treatment Effect

Lokesh Nagalapatti, Pranava Singhal, Avishek Ghosh, Sunita Sarawagi
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:37237-37259, 2024.

Abstract

Given a dataset of individuals each described by a covariate vector, a treatment, and an observed outcome on the treatment, the goal of the individual treatment effect (ITE) estimation task is to predict outcome changes resulting from a change in treatment. A fundamental challenge is that in the observational data, a covariate’s outcome is observed only under one treatment, whereas we need to infer the difference in outcomes under two different treatments. Several existing approaches address this issue through training with inferred pseudo-outcomes, but their success relies on the quality of these pseudo-outcomes. We propose PairNet, a novel ITE estimation training strategy that minimizes losses over pairs of examples based on their factual observed outcomes. Theoretical analysis for binary treatments reveals that PairNet is a consistent estimator of ITE risk, and achieves smaller generalization error than baseline models. Empirical comparison with thirteen existing methods across eight benchmarks, covering both discrete and continuous treatments, shows that PairNet achieves significantly lower ITE error compared to the baselines. Also, it is model-agnostic and easy to implement.

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-nagalapatti24a, title = {{P}air{N}et: Training with Observed Pairs to Estimate Individual Treatment Effect}, author = {Nagalapatti, Lokesh and Singhal, Pranava and Ghosh, Avishek and Sarawagi, Sunita}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {37237--37259}, 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/nagalapatti24a/nagalapatti24a.pdf}, url = {https://proceedings.mlr.press/v235/nagalapatti24a.html}, abstract = {Given a dataset of individuals each described by a covariate vector, a treatment, and an observed outcome on the treatment, the goal of the individual treatment effect (ITE) estimation task is to predict outcome changes resulting from a change in treatment. A fundamental challenge is that in the observational data, a covariate’s outcome is observed only under one treatment, whereas we need to infer the difference in outcomes under two different treatments. Several existing approaches address this issue through training with inferred pseudo-outcomes, but their success relies on the quality of these pseudo-outcomes. We propose PairNet, a novel ITE estimation training strategy that minimizes losses over pairs of examples based on their factual observed outcomes. Theoretical analysis for binary treatments reveals that PairNet is a consistent estimator of ITE risk, and achieves smaller generalization error than baseline models. Empirical comparison with thirteen existing methods across eight benchmarks, covering both discrete and continuous treatments, shows that PairNet achieves significantly lower ITE error compared to the baselines. Also, it is model-agnostic and easy to implement.} }
Endnote
%0 Conference Paper %T PairNet: Training with Observed Pairs to Estimate Individual Treatment Effect %A Lokesh Nagalapatti %A Pranava Singhal %A Avishek Ghosh %A Sunita Sarawagi %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-nagalapatti24a %I PMLR %P 37237--37259 %U https://proceedings.mlr.press/v235/nagalapatti24a.html %V 235 %X Given a dataset of individuals each described by a covariate vector, a treatment, and an observed outcome on the treatment, the goal of the individual treatment effect (ITE) estimation task is to predict outcome changes resulting from a change in treatment. A fundamental challenge is that in the observational data, a covariate’s outcome is observed only under one treatment, whereas we need to infer the difference in outcomes under two different treatments. Several existing approaches address this issue through training with inferred pseudo-outcomes, but their success relies on the quality of these pseudo-outcomes. We propose PairNet, a novel ITE estimation training strategy that minimizes losses over pairs of examples based on their factual observed outcomes. Theoretical analysis for binary treatments reveals that PairNet is a consistent estimator of ITE risk, and achieves smaller generalization error than baseline models. Empirical comparison with thirteen existing methods across eight benchmarks, covering both discrete and continuous treatments, shows that PairNet achieves significantly lower ITE error compared to the baselines. Also, it is model-agnostic and easy to implement.
APA
Nagalapatti, L., Singhal, P., Ghosh, A. & Sarawagi, S.. (2024). PairNet: Training with Observed Pairs to Estimate Individual Treatment Effect. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:37237-37259 Available from https://proceedings.mlr.press/v235/nagalapatti24a.html.

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