Outcome-Assisted Multiple Imputation of Missing Treatments

TL;DR

This paper introduces outcome-assisted multiple imputation for missing treatments in observational studies, improving bias reduction and inference accuracy by incorporating outcome information into the imputation process.

Contribution

It develops a novel outcome-assisted imputation method that combines outcome modeling with propensity scores, enhancing treatment effect estimation accuracy.

Findings

Outcome-assisted imputation reduces bias in treatment effect estimates.

Simulation results show improved inferential properties over traditional methods.

Application to survey data demonstrates practical utility.

Abstract

We provide guidance on multiple imputation of missing at random treatments in observational studies. Specifically, analysts should account for both covariates and outcomes, i.e., not just use propensity scores, when imputing the missing treatments. To do so, we develop outcome-assisted multiple imputation of missing treatments: the analyst fits a regression for the outcome on the treatment indicator and covariates, which is used to sharpen the predictive probabilities for missing treatments under an estimated propensity score model. We derive an expression for the bias of the inverse probability weighted estimator for the average treatment effect under multiple imputation of missing treatments, and we show theoretically that this bias can be made small by using outcome-assisted multiple imputation. Simulations demonstrate empirically that outcome-assisted multiple imputation can offer better inferential properties than using the treatment assignment model alone. We illustrate the procedure in an analysis of data from the National Longitudinal Survey of Youth.