On the statistical role of inexact matching in observational studies
研究发现非精确匹配不能像精确匹配那样有效控制偏差和保证无假设推断，建议在非精确匹配后额外进行基于模型的协变量调整，并证明匹配能增强后续参数分析对模型误设的稳健性。
Summary 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. In this paper we show 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 such robustness is the primary statistical role of inexact matching.