Title: Pseudo-outcome Imputation with Post-treatment Variables for Individual Treatment Effect Estimation
Primary area: causal reasoning
Keywords: causal inference, individual treatment effects, post-treatment variables

Abstract: The estimation of individual treatment effects (ITE) focuses on predicting the outcome changes that result from a change in treatment. A fundamental challenge in observational data is that while we need to infer outcome differences under alternative treatments, we can only observe each individual's outcome under a single treatment.  Existing approaches address this limitation either by training with inferred pseudo-outcomes or by creating matched instance pairs. However, these methods overlook post-treatment variables that not only directly influence outcomes but are also affected by exogenous noise. This oversight prevents existing methods from fully capturing outcome variability, resulting in increased variance in counterfactual predictions. In this paper, we introduce PIPCFR, a novel approach that incorporates post-treatment variables through a teacher-student architecture to improve pseudo-outcome imputation. We analyze the challenges inherent in utilizing post-treatment variables and establish a novel theoretical bound for ITE risk that explicitly connects post-treatment variables to ITE estimation accuracy. Unlike existing methods that ignore these variables or impose restrictive assumptions, PIPCFR learns effective representations that preserve informative components while mitigating bias. Empirical evaluations across diverse datasets demonstrate that PIPCFR achieves significantly lower ITE errors compared to existing methods.

TL;DR: We propose a novel approach that incorporates post-treatment variables through a teacher-student architecture to improve pseudo-outcome imputation.