A Primer on Propensity Score Analysis☆

This article discusses the role that propensity score analysis can play in assessing the effects of interventions. It mostly focuses on identifying the range of solutions to practical problems that occur in propensity score analysis, especially with regard to propensity score construction (logistic regression, classification trees, ensemble methods), balancing (significance tests, other metrics), and analysis (matching, stratifying, weighting, covariance). Throughout, the article will identify particularly important or common pitfalls that need to be avoided in these analyses. The article ends with a discussion of the comparative advantages and disadvantages of propensity scores compared to alternative analytic and design options.

Keywords: Propensity score, Matching, Nonrandomized experiment, Randomized experiment, Strong ignorability