Title: Sinkhorn Discrepancy for Counterfactual Generalization. Abstract: Estimating individual treatment effects from observational data is very challenging due to the existence of treatment selection bias.
Most existing representation-based methods mitigate this issue by aligning distributions of different treatment groups in the representation space. However, they still suffer from two critical problems: (1) Mini-batch Sampling Effects (MSE), where the alignment easily fails due to the outcome imbalance or outliers in the batch; (2) Unobserved Confounder Effects (UCE), where the unobserved confounders damage the correct alignment. To tackle these problems, we propose a principled approach named Entire Space CounterFactual Regression (ESCFR) based on a generalized sinkhorn discrepancy for distribution alignment within the stochastic optimal transport framework. Based on the framework, we propose a relaxed mass preserving regularizer to address the MSE issue and design a proximal factual outcome regularizer to handle the UCE issue. Extensive experiments demonstrate that our proposed ESCFR can successfully tackle the treatment selection bias and achieve significantly better performance than state-of-the-art methods.