10/5/2017 | 4:15pm | E51-335

Reception to follow.

Sam Pimentel

Assistant Professor
University of California

Abstract: How do health outcomes for newly-trained surgeons' patients compare with those for patients of experienced surgeons? To answer this question using data from Medicare, we introduce a new form of matching that pairs patients of 1252 new surgeons to patients of experienced surgeons, exactly balancing 176 surgical procedures and closely balancing 2.9 million finer patient categories. The new matching algorithm (which uses penalized network flows) exploits a sparse network to quickly optimize a match two orders of magnitude larger than usual in statistical matching. This allows extensive use of a new form of marginal balance constraint called refined covariate balance.

Bio: Sam Pimentel is an Assistant Professor in the Statistics Department at the University of California, Berkeley. He received his PhD from the Statistics Department in the Wharton School at University of Pennsylvania, where he was advised by Paul Rosenbaum. His research focuses on causal inference in observational studies, especially matching methodologies that incorporate combinatorial optimization. He is a recipient of the National Defense Science & Engineering Graduate Fellowship and the Thomas Ten Have Award for "exceptionally creative or skillful research in causal inference."