An introduction to matching methods for causal inference and their implementation in Stata

Matching, especially in its propensity-score flavors, has become an extremely popular evaluation method. Matching is, in fact, the best-available method for selecting a matched (or reweighted) comparison group that looks like the treatment group of interest. In this talk, I will introduce matching methods within the general problem of causal inference, highlight their strengths and weaknesses, and offer a brief overview of different matching estimators. Using psmatch2, I will then step through a practical example in Stata that is based on real data. I will then show how to implement some of these estimators, as well as highlight a number of implementational issues.