Online Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 500 - Statistical Learning
Type: Contributed
Date/Time: Thursday, August 6, 2020 : 10:00 AM to 2:00 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #313176
Title: Using Machine Learning to Improve Propensity Score Matching Methods in Observational Studies
Author(s): Nan Zhang* and Daniel J. Graham
Companies: Imperial College London and Imperial College London
Keywords: causal inference; propensity score matching; machine learning techniques; benchmarking

Propensity score matching (PSM) methods are a commonly used approach to reduce selection bias in estimating average treatment effects. In addition to traditional Logistic Regression (LR) models, recent machine learning tools are also applicable to estimate propensity scores. In this paper, we apply state-of-the-art machine learning techniques to improve propensity score estimation and benchmark their performance with the traditional LR models. We perform comprehensive simulations, implementing 8 scenarios that mimic typical characteristics of both simple and complicated data sets. The simulation design considers: 1) high-dimensional covariates, 2) correlation of the covariates, and 3) presence of unknown clustering. Performance of the models are evaluated by propensity score prediction accuracy, achieved covariate balance and mean squared error of treatment effect estimates. Our results suggest that machine learning based PSM led to superior reduction of bias in complicated datasets scenarios.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2020 program