Large, Sparse Optimal Matching in an Observational Study of Surgical Outcomes

He will talk about how health outcomes for newly-trained surgeons' patients compare with those for patients of experienced surgeons. To answer this question using data from Medicare, he will talk about introducing 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, and allowing for extensive use of a new form of marginal balance constraint.