Multivariate Matching Methods That Are Monotonic Imbalance Bounding

• 成果类型: Article
• 署名作者: Iacus, Stefano M.; King, Gary; Porro, Giuseppe
• 署名单位: University of Milan; Harvard University; University of Trieste
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/jasa.2011.tm09599
• 发表日期: 2011
• 页码: 345-361
• 关键词: Bias

摘要

We introduce a new Monotonic Imbalance Bounding (MIB) class of matching methods for causal inference with a surprisingly large number of attractive statistical properties. MIB generalizes and extends in several new directions the only existing class, Equal Percent Bias Reducing (EPBR), which is designed to satisfy weaker properties and only in expectation. We also offer strategies to obtain specific members of the MIB class, and analyze in more detail a member of this class, called Coarsened Exact Matching, whose properties we analyze from this new perspective. We offer a variety of analytical results and numerical simulations that demonstrate how members of the MIB class can dramatically improve inferences relative to EPBR-based matching methods.