Estimating Marginal Treatment Effects Using Quantile Regression Imputation Under a Non-Ignorable Selection Assumption

Music City Center 

Most treatment effect estimation studies assume missing at random (MAR), meaning that treatment selection probability depends only on observed covariates. However, in real-world applications, selection probability often depends on potential outcomes, leading to biased treatment effect estimates if not properly addressed. Instrumental variables (IVs) offer a potential solution to unobserved confounding, but identifying valid IVs and verifying their assumptions is challenging. This talk proposes an iterative estimation-solving algorithm that bypasses the IV assumption and imputes potential outcomes using semiparametric quantile regression under a missing not at random (MNAR) framework. We discuss model identification under MNAR and establish the theoretical properties of the proposed estimator, including its convergence and large-sample behavior. Simulation studies and a real-data application further validate the effectiveness of our approach.