ADVERSARIALLY BALANCED REPRESENTATION FOR CONTINUOUS TREATMENT EFFECT ESTIMATIONDownload PDF

22 Sept 2022 (modified: 13 Feb 2023)ICLR 2023 Conference Withdrawn SubmissionReaders: Everyone
Abstract: Estimating the individual treatment effect (ITE) requires covariate balance among different treatment groups, and machine learning models have shown great promise in learning a balanced representation of covariates. In contrast with binary treatments for which learning such a representation has been widely studied, the more practical yet complicated continuous treatment setting has remained relatively under-explored. Adopting an information-theoretic approach, we introduce a novel mutual information (MI)-based objective for continuous treatment effect estimation. Leveraging variational approximation to optimize MI terms in our objective, we propose a method called Adversarial CounterFactual Regression (ACFR). ACFR aligns the representation of covariates through an adversarial game and predicts the potential outcomes using a contribution-constraining hypothesis network. Comparison of ACFR against state-of-the-art methods on semi-synthetic datasets demonstrates its superiority in individual-level metrics.
Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.
No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.
Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics
Submission Guidelines: Yes
Please Choose The Closest Area That Your Submission Falls Into: Deep Learning and representational learning
11 Replies

Loading