Counterfactual Prediction for Outcome-Oriented Treatments

Hao Zou, Bo Li, Jiangang Han, Shuiping Chen, Xuetao Ding, Peng Cui
Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27693-27706, 2022.

Abstract

Large amounts of efforts have been devoted into learning counterfactual treatment outcome under various settings, including binary/continuous/multiple treatments. Most of these literature aims to minimize the estimation error of counterfactual outcome for the whole treatment space. However, in most scenarios when the counterfactual prediction model is utilized to assist decision-making, people are only concerned with the small fraction of treatments that can potentially induce superior outcome (i.e. outcome-oriented treatments). This gap of objective is even more severe when the number of possible treatments is large, for example under the continuous treatment setting. To overcome it, we establish a new objective of optimizing counterfactual prediction on outcome-oriented treatments, propose a novel Outcome-Oriented Sample Re-weighting (OOSR) method to make the predictive model concentrate more on outcome-oriented treatments, and theoretically analyze that our method can improve treatment selection towards the optimal one. Extensive experimental results on both synthetic datasets and semi-synthetic datasets demonstrate the effectiveness of our method.

Cite this Paper

BibTeX
@InProceedings{pmlr-v162-zou22a, title = {Counterfactual Prediction for Outcome-Oriented Treatments}, author = {Zou, Hao and Li, Bo and Han, Jiangang and Chen, Shuiping and Ding, Xuetao and Cui, Peng}, booktitle = {Proceedings of the 39th International Conference on Machine Learning}, pages = {27693--27706}, year = {2022}, editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan}, volume = {162}, series = {Proceedings of Machine Learning Research}, month = {17--23 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v162/zou22a/zou22a.pdf}, url = {https://proceedings.mlr.press/v162/zou22a.html}, abstract = {Large amounts of efforts have been devoted into learning counterfactual treatment outcome under various settings, including binary/continuous/multiple treatments. Most of these literature aims to minimize the estimation error of counterfactual outcome for the whole treatment space. However, in most scenarios when the counterfactual prediction model is utilized to assist decision-making, people are only concerned with the small fraction of treatments that can potentially induce superior outcome (i.e. outcome-oriented treatments). This gap of objective is even more severe when the number of possible treatments is large, for example under the continuous treatment setting. To overcome it, we establish a new objective of optimizing counterfactual prediction on outcome-oriented treatments, propose a novel Outcome-Oriented Sample Re-weighting (OOSR) method to make the predictive model concentrate more on outcome-oriented treatments, and theoretically analyze that our method can improve treatment selection towards the optimal one. Extensive experimental results on both synthetic datasets and semi-synthetic datasets demonstrate the effectiveness of our method.} }
Endnote
%0 Conference Paper %T Counterfactual Prediction for Outcome-Oriented Treatments %A Hao Zou %A Bo Li %A Jiangang Han %A Shuiping Chen %A Xuetao Ding %A Peng Cui %B Proceedings of the 39th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2022 %E Kamalika Chaudhuri %E Stefanie Jegelka %E Le Song %E Csaba Szepesvari %E Gang Niu %E Sivan Sabato %F pmlr-v162-zou22a %I PMLR %P 27693--27706 %U https://proceedings.mlr.press/v162/zou22a.html %V 162 %X Large amounts of efforts have been devoted into learning counterfactual treatment outcome under various settings, including binary/continuous/multiple treatments. Most of these literature aims to minimize the estimation error of counterfactual outcome for the whole treatment space. However, in most scenarios when the counterfactual prediction model is utilized to assist decision-making, people are only concerned with the small fraction of treatments that can potentially induce superior outcome (i.e. outcome-oriented treatments). This gap of objective is even more severe when the number of possible treatments is large, for example under the continuous treatment setting. To overcome it, we establish a new objective of optimizing counterfactual prediction on outcome-oriented treatments, propose a novel Outcome-Oriented Sample Re-weighting (OOSR) method to make the predictive model concentrate more on outcome-oriented treatments, and theoretically analyze that our method can improve treatment selection towards the optimal one. Extensive experimental results on both synthetic datasets and semi-synthetic datasets demonstrate the effectiveness of our method.
APA
Zou, H., Li, B., Han, J., Chen, S., Ding, X. & Cui, P.. (2022). Counterfactual Prediction for Outcome-Oriented Treatments. Proceedings of the 39th International Conference on Machine Learning, in Proceedings of Machine Learning Research 162:27693-27706 Available from https://proceedings.mlr.press/v162/zou22a.html.

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