An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies
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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.
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The record
- Venue
- Multivariate Behavioral Research
- Topic
- Advanced Causal Inference Techniques
- Field
- Mathematics
- Canadian institutions
- Institute for Clinical Evaluative SciencesUniversity of Toronto
- Funders
- Ontario Ministry of Health and Long-Term CareInstitute for Clinical Evaluative Sciences
- Keywords
- Propensity score matchingObservational studyCovariateConfoundingStatisticsInverse probability weightingEconometricsMathematics
- Has abstract in OpenAlex
- yes