A Non-parametric Direct Learning Approach to Heterogeneous Treatment Effect Estimation under Unmeasured Confounding

TL;DR

This paper introduces a non-parametric method for estimating heterogeneous treatment effects using instrumental variables, effectively addressing unmeasured confounding in observational and experimental studies.

Contribution

It proposes a general framework for CATE estimation with unmeasured confounders, enhancing efficiency and robustness over existing methods.

Findings

Framework performs well in simulations

Method demonstrates robustness to model misspecification

Real data example confirms practical utility

Abstract

In many social, behavioral, and biomedical sciences, treatment effect estimation is a crucial step in understanding the impact of an intervention, policy, or treatment. In recent years, an increasing emphasis has been placed on heterogeneity in treatment effects, leading to the development of various methods for estimating Conditional Average Treatment Effects (CATE). These approaches hinge on a crucial identifying condition of no unmeasured confounding, an assumption that is not always guaranteed in observational studies or randomized control trials with non-compliance. In this paper, we proposed a general framework for estimating CATE with a possible unmeasured confounder using Instrumental Variables. We also construct estimators that exhibit greater efficiency and robustness against various scenarios of model misspecification. The efficacy of the proposed framework is demonstrated through simulation studies and a real data example.