Optimal Transport for Treatment Effect Estimation

TL;DR

This paper introduces ESCFR, a novel optimal transport-based method that addresses mini-batch sampling effects and unobserved confounders to improve treatment effect estimation from observational data.

Contribution

The paper proposes a new approach called ESCFR that incorporates stochastic optimal transport with regularizers to mitigate key biases in treatment effect estimation.

Findings

ESCFR outperforms existing methods in treatment effect estimation.

The relaxed mass-preserving regularizer reduces mini-batch sampling bias.

The proximal factual outcome regularizer addresses unobserved confounders.

Abstract

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.