Optimal Matching for Observational Studies
作者:
PaulR. Rosenbaum,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1989)
卷期:
Volume 84,
issue 408
页码: 1024-1032
ISSN:0162-1459
年代: 1989
DOI:10.1080/01621459.1989.10478868
出版商: Taylor & Francis Group
关键词: Graph algorithms;Network flow;Propensity score;Statistical computing
数据来源: Taylor
摘要:
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.
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