Covariate balancing with measurement error

TL;DR

This paper investigates the effects of measurement error on covariate balancing in causal inference and proposes correction strategies to improve bias reduction and covariate balance.

Contribution

It introduces novel measurement error correction methods for covariate balancing, ensuring unbiased treatment effect estimates despite measurement inaccuracies.

Findings

Naively ignoring measurement error increases covariate imbalance.

Proposed correction strategies recover covariate balance.

Correction methods eliminate bias in treatment effect estimation.

Abstract

In recent years, there is a growing body of causal inference literature focusing on covariate balancing methods. These methods eliminate observed confounding by equalizing covariate moments between the treated and control groups. The validity of covariate balancing relies on an implicit assumption that all covariates are accurately measured, which is frequently violated in observational studies. Nevertheless, the impact of measurement error on covariate balancing is unclear, and there is no existing work on balancing mismeasured covariates adequately. In this article, we show that naively ignoring measurement error reversely increases the magnitude of covariate imbalance and induces bias to treatment effect estimation. We then propose a class of measurement error correction strategies for the existing covariate balancing methods. Theoretically, we show that these strategies successfully recover balance for all covariates, and eliminate bias of treatment effect estimation. We assess the proposed correction methods in simulation studies and real data analysis.