Estimating Conditional Average Treatment Effects with Missing Treatment Information
This addresses a widespread but understudied issue in causal inference for applications like healthcare or policy analysis, though it appears incremental as it builds on existing CATE estimation with a novel adaptation.
The paper tackles the problem of estimating conditional average treatment effects when treatment information is missing, addressing covariate shifts between treated/control and observed/missing populations, and proposes MTRNet, which improves over state-of-the-art methods in experiments.
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.