Randomized experiments are the gold standard for making causal inferences — without randomization, post-treatment group differences may be confounded by pre-existing group differences (i.e., selection bias). But randomization is not always possible, practical or ethical in the social, behavioral and education sciences. Alternative, quasi-experimental methods are needed in the absence — or failure — of randomization. 

Propensity score analysis (PSA) is a broad collection of methods that aims to statistically equate two or more groups on a set of observed covariates as a means for minimizing selection bias. This presentation will provide an introduction to PSA, with emphasis on propensity score matching, sub-classification and weighting. Empirical examples will be provided to demonstrate the methods’ practical application.