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.
aSchool of Social Science, Humanities and Arts, University of California, Merced, Merced, CA
bInstitute for Policy Research, Northwestern University, Evanston, IL
Address correspondence to William R. Shadish, PhD, School of Social Science, Humanities and Arts, University of California, Merced, 5200 N. Lake Rd, Merced, CA 95343.
☆ The authors were supported in part by grant R305U070003 from the Institute for Educational Sciences, U.S. Department of Education. The second author was also supported by grants from the Spencer Foundation and W.T. Grant Foundation.