Improving propensity score weighting using machine learning

2009/12/03 by Brian K. Lee, Justin Lessler, Elizabeth A. Stuart · 10 citations
Mathematics · #Advanced Causal Inference Techniques #Statistical Methods and Bayesian Inference #Statistical Methods in Clinical Trials

paper · pdf · doi:10.1002/sim.3782

openalex publication_date 2009/12/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29

Abstract

Machine learning techniques such as classification and regression trees (CART) have been suggested as promising alternatives to logistic regression for the estimation of propensity scores. The authors examined the performance of various CART-based propensity score models using simulated data. Hypothetical studies of varying sample sizes (n=500, 1000, 2000) with a binary exposure, continuous outcome, and 10 covariates were simulated under seven scenarios differing by degree of non-linear and non-additive associations between covariates and the exposure. Propensity score weights were estimated using logistic regression (all main effects), CART, pruned CART, and the ensemble methods of bagged CART, random forests, and boosted CART. Performance metrics included covariate balance, standard error, per cent absolute bias, and 95 per cent confidence interval (CI) coverage. All methods displayed generally acceptable performance under conditions of either non-linearity or non-additivity alone. However, under conditions of both moderate non-additivity and moderate non-linearity, logistic regression had subpar performance, whereas ensemble methods provided substantially better bias reduction and more consistent 95 per cent CI coverage. The results suggest that ensemble methods, especially boosted CART, may be useful for propensity score weighting.

Citations

• Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)
• A working guide to boosted regression trees
• Doubly Robust Estimation in Missing Data and Causal Inference Models
• The central role of the propensity score in observational studies for causal effects

Cited by

• Improving Variance and Confidence Interval Estimation in Small-Sample Propensity Score Analyses: Bootstrap vs. Asymptotic Methods
• Recent Developments in Causal Inference and Machine Learning
• The Blessings of Multiple Causes
• Matching Methods for Confounder Adjustment: An Addition to the Epidemiologist’s Toolbox
• Balancing vs modeling approaches to weighting in practice
• On variance estimation of the inverse probability‐of‐treatment weighting estimator: A tutorial for different types of propensity score weights
• Bayesian Network Propensity Score to Evaluate Treatment Effects in Observational Studies
• Matching-Based Nonparametric Estimation of Group Average Treatment Effects
• What to Observe When Assuming Selection on Observables
• Machine Learning: An Applied Econometric Approach

Related

• Propensity score estimation using classification and regression trees in the presence of missing covariate data
• Machine learning for propensity score estimation: A systematic review and reporting guidelines.
• Robust Estimating Method for Propensity Score Models and its Application to Some Causal Estimands: A review and proposal
• Logistic regression frequently outperformed propensity score methods, especially for large datasets: a simulation study
• When is the estimated propensity score better? High-dimensional analysis and bias correction