Title: ADVERSARIALLY BALANCED REPRESENTATION FOR CONTINUOUS TREATMENT EFFECT ESTIMATION. 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.