论文标题
关于观察研究中不进行匹配的统计作用
On the statistical role of inexact matching in observational studies
论文作者
论文摘要
在观察性因果推断中，确切的协变量匹配起着两个统计角色：（i）由于测量的混杂而有效控制偏见； （ii）基于随机测试，它证明无假设的推断是合理的。本文表明，不精确的协变量匹配并不总是扮演相同的角色。我们发现，不确定的匹配通常会落后于统计上有意义的偏见，并且这种偏见使标准的随机测试无效。因此，我们建议在匹配不进行的其他基于模型的协变量调整。在本地错误指定的框架中，我们证明匹配使随后的参数分析对模型选择或错误指定的敏感性降低。我们认为，获得这种鲁棒性是不进行匹配的主要统计作用。
In observational causal inference, exact covariate matching plays two statistical roles: (i) it effectively controls for bias due to measured confounding; (ii) it justifies assumption-free inference based on randomization tests. This paper shows that inexact covariate matching does not always play these same roles. We find that inexact matching often leaves behind statistically meaningful bias and that this bias renders standard randomization tests asymptotically invalid. We therefore recommend additional model-based covariate adjustment after inexact matching. In the framework of local misspecification, we prove that matching makes subsequent parametric analyses less sensitive to model selection or misspecification. We argue that gaining this robustness is the primary statistical role of inexact matching.
