Introduction to Propensity Score Analysis

Methods of causal inference have undergone a significant change in the past 30 years as researchers have recognized the need to develop more efficient approaches to control extraneous factors. The interest in seeking consistent and efficient estimators of program effectiveness has led to a surge in work focusing on estimating average treatment effects under various sets of assumptions. Propensity score analysis is a relatively new and innovative class of statistical methods that has proven useful for evaluating treatment effects when using nonexperimental or observational data. Specifically, propensity score analysis offers an approach to program evaluation when randomized clinical trials are not feasible, unethical, or when researchers need to assess treatment effects from survey data, census data, administrative data, or other types of data collected through the observation of systems.