A0229
Title: Randomization-based inference for average treatment effects in inexactly matched observational studies
Authors: Jianan Zhu - New York University (United States) [presenting]
Jeffrey Zhang - University of Pennsylvania (United States)
Zijian Guo - Rutgers University (United States)
Siyu Heng - New York University (United States)
Abstract: Matching is a widely used causal inference study design in observational studies. It seeks to mimic a randomized experiment by forming matched sets of treated and control units based on proximity in covariates. Ideally, treated units are exactly matched with controls for the covariates and randomization-based inference for the treatment effect can then be conducted under the ignorability assumption. However, matching is typically inexact when continuous covariates or many covariates exist. Previous studies have routinely ignored inexact matching in the downstream randomization-based inference as long as some covariate balance criteria are satisfied. Some recent studies found that this routine practice can cause severe bias. However, these inference methods focus on the constant treatment effect and are not directly applicable to the average treatment effect. To address this important gap, a new framework is proposed inverse post-matching probability weighting (IPPW) for randomization-based inference of average treatment effects under inexact matching. Compared with the routinely used randomization-based inference framework based on the difference-in-means estimator for average treatment effects, the proposed IPPW framework can substantially reduce bias due to inexact matching and improve the coverage rate. The framework can also be extended to the instrumental variable settings to simultaneously address the bias due to inexact matching and unmeasured confounding bias.
