Optimal Matching for Observational Studies
本文利用网络流理论，在观察性研究中构建最优匹配样本，解决了传统贪婪启发式方法产生的次优问题，并扩展到多对照、可变对照数及平衡匹配等新问题。
Abstract Matching is a common method of adjustment in observational studies. Currently, matched samples are constructed using greedy heuristics (or “stepwise” procedures) that produce, in general, suboptimal matchings. With respect to a particular criterion, a matched sample is suboptimal if it could be improved by changing the controls assigned to specific treated units, that is, if it could be improved with the data at hand. Here, optimal matched samples are obtained using network flow theory. In addition to providing optimal matched-pair samples, this approach yields optimal constructions for several statistical matching problems that have not been studied previously, including the construction of matched samples with multiple controls, with a variable number of controls, and the construction of balanced matched samples that combine features of pair matching and frequency matching. Computational efficiency is discussed. Extensive use is made of ideas from two essentially disjoint literatures, namely statistical matching in observational studies and graph algorithms for matching. The article contains brief reviews of both topics.