Matching Estimators for Causal Effects of Multiple Treatments

TL;DR

This paper introduces a nearest-neighbors matching estimator tailored for multiple treatments, demonstrating its effectiveness through simulations and real-world application to medication effects on pain.

Contribution

It proposes a novel matching estimator for multiple treatments and provides inference methods validated via simulations and a practical case study.

Findings

Estimator is precise with coverage close to nominal levels.

Simulation results support the estimator's reliability.

Application reveals differences in medication effects on pain.

Abstract

Matching estimators for average treatment effects are widely used in the binary treatment setting, in which missing potential outcomes are imputed as the average of observed outcomes of all matches for each unit. With more than two treatment groups, however, estimation using matching requires additional techniques. In this paper, we propose a nearest-neighbors matching estimator for use with multiple, nominal treatments, and use simulations to show that this method is precise and has coverage levels that are close to nominal. In addition, we implement the proposed inference methods to examine the effects of different medication regimens on long-term pain for patients experiencing motor vehicle collision.