Estimating Conditional Average Treatment Effects with Missing Treatment Information

TL;DR

This paper addresses the challenge of estimating conditional average treatment effects when treatment data is missing, introducing a novel algorithm that leverages balanced representations to improve estimation accuracy under covariate shifts.

Contribution

The paper identifies covariate shifts in missing treatment scenarios, derives a theoretical generalization bound, and proposes MTRNet, a new domain adaptation-based method for better CATE estimation.

Findings

MTRNet outperforms existing methods on semi-synthetic data.

Theoretical analysis highlights the impact of covariate shifts.

Empirical results demonstrate substantial improvements in real-world datasets.

Abstract

Estimating conditional average treatment effects (CATE) is challenging, especially when treatment information is missing. Although this is a widespread problem in practice, CATE estimation with missing treatments has received little attention. In this paper, we analyze CATE estimation in the setting with missing treatments where unique challenges arise in the form of covariate shifts. We identify two covariate shifts in our setting: (i) a covariate shift between the treated and control population; and (ii) a covariate shift between the observed and missing treatment population. We first theoretically show the effect of these covariate shifts by deriving a generalization bound for estimating CATE in our setting with missing treatments. Then, motivated by our bound, we develop the missing treatment representation network (MTRNet), a novel CATE estimation algorithm that learns a balanced representation of covariates using domain adaptation. By using balanced representations, MTRNet provides more reliable CATE estimates in the covariate domains where the data are not fully observed. In various experiments with semi-synthetic and real-world data, we show that our algorithm improves over the state-of-the-art by a substantial margin.