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.

我们提出了一种名为整体空间因果回归（ESCFR）的原则性方法，该方法利用随机最优输运的框架来应对观察数据中的治疗选择偏倚问题，并通过放松保质保量约束和设计近端事实后果约束来解决小批量采样效应和未观测混淆因素的影响，实验证明我们的方法能够成功应对治疗选择偏倚，并取得明显优于现有方法的性能。