An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies

TLDR

The propensity score is the probability of treatment assignment conditional on observed baseline characteristics and serves as a balancing score that mimics randomized trials by equalizing covariate distributions between treated and untreated groups. The study aims to describe four propensity score methods—matching, stratification, inverse probability weighting, and covariate adjustment—along with balance diagnostics and causal effect estimands. The author explains how each method operates, how balance diagnostics assess model adequacy, and contrasts regression-based with propensity score-based analyses for observational data.

Abstract

The propensity score is the probability of treatment assignment conditional on observed baseline characteristics. The propensity score allows one to design and analyze an observational (nonrandomized) study so that it mimics some of the particular characteristics of a randomized controlled trial. In particular, the propensity score is a balancing score: conditional on the propensity score, the distribution of observed baseline covariates will be similar between treated and untreated subjects. I describe 4 different propensity score methods: matching on the propensity score, stratification on the propensity score, inverse probability of treatment weighting using the propensity score, and covariate adjustment using the propensity score. I describe balance diagnostics for examining whether the propensity score model has been adequately specified. Furthermore, I discuss differences between regression-based methods and propensity score-based methods for the analysis of observational data. I describe different causal average treatment effects and their relationship with propensity score analyses.