Estimating conditional average treatment effect from observational data is highly challenging due to the existence of treatment selection bias. Prevalent methods mitigate this issue by aligning distributions of different treatment groups in the latent space. However, there are two critical problems that these methods fail to address: (1) mini-batch sampling effects (MSE), which causes misalignment in non-ideal mini-batches with outcome imbalance and outliers; (2) unobserved confounder effects (UCE), which results in inaccurate discrepancy calculation due to the neglect of unobserved confounders. To tackle these problems, we propose a principled approach named Entire Space CounterFactual Regression (ESCFR), which is a new take on optimal transport in the context of causality. Specifically, based on the framework of stochastic optimal transport, 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.

翻译：从观测数据中估计条件平均处理效应极具挑战性，原因在于存在处理选择偏差。现有主流方法通过对齐潜在空间中不同处理组的分布来缓解这一问题。然而，这些方法未能解决两个关键问题：（1）小批量采样效应（MSE），即在非理想小批量中存在结果不平衡和异常值时导致分布错位；（2）未观测混杂效应（UCE），即因忽略未观测混杂因素而导致的不精确差异计算。为应对这些问题，我们提出了一种名为全空间反事实回归（ESCFR）的原则性方法，这是最优传输在因果关系领域的新应用。具体而言，基于随机最优传输框架，我们提出了一种松弛的质量守恒正则化器以解决MSE问题，并设计了近端事实结果正则化器来处理UCE问题。大量实验表明，我们提出的ESCFR能够成功解决处理选择偏差，并在性能上显著优于现有最先进方法。