Propensity Score Matching and Subclassification in Observational Studies with Multi-level Treatments

TL;DR

This paper introduces new propensity score matching and subclassification methods for estimating treatment effects in observational studies with multiple treatment levels, demonstrating their effectiveness through simulations and real data analysis.

Contribution

It extends existing binary treatment methods to multi-level treatments using weak unconfoundedness, enabling bias removal with scalar functions of pre-treatment variables.

Findings

Methods effectively reduce bias in multi-level treatment settings.

Simulation results show improved finite sample performance.

Application to fibromyalgia treatments illustrates practical utility.

Abstract

In this paper, we develop new methods for estimating average treatment effects in observational studies, focusing on settings with more than two treatment levels under unconfoundedness given pre-treatment variables. We emphasize subclassification and matching methods which have been found to be effective in the binary treatment literature and which are among the most popular methods in that setting. Whereas the literature has suggested that these particular propensity-based methods do not naturally extend to the multi-level treatment case, we show, using the concept of weak unconfoundedness, that adjusting for or matching on a scalar function of the pre-treatment variables removes all biases associated with observed pre-treatment variables. We apply the proposed methods to an analysis of the effect of treatments for fibromyalgia. We also carry out a simulation study to assess the finite sample performance of the methods relative to previously proposed methods.