Dear Applied Statistics Community,
Please join us this Wednesday when Alberto Abadie, Professor of Public Policy, will present ``A General Theory of Matching Estimation", joint work with Guido Imbens. Alberto provided the following abstract for his talk:
Matching methods provide simple and intuitive tools for adjusting the distribution of covariates among samples from different populations. Probably because of their transparency and intuitive appeal, matching methods are widely used in evaluation research to estimate treatment effects when all treatment confounders are observed (Rubin, 1973, 1977; Rosenbaum, 2002). In spite of their popularity, the problem of establishing the large sample distribution of matching estimators remains largely unsolved, with the exception of some special cases (see Abadie and Imbens, 2006). The reason is that matching estimators are non-smooth functionals of the data, which makes their large sample theory particularly challenging. This talk will describe a new general method to establish the large sample distribution of matching estimators. As an example of the applicability of the method, we will describe how to derive the distribution of matching estimators when matching is carried out without replacement, a result previously unavailable in the literature. We will also discuss how to adjust the standard errors for propensity score matching estimators to take into account first step estimation of the propensity score, a result also previously unavailable.
The Applied Statistics Workshop meets each Wednesday at 12 noon in K-354 CGIS-Knafel (1737 Cambridge St). The workshop begins with a light lunch and presentations usually start around 1215 and last until about 130 pm.
Justin Grimmer