Model misspecification is a potential problem for any parametric-model based analysis. However, the measurement and consequences of model misspecification have not been well formalized in the context of causal inference. A measure of model misspecification is proposed, and the consequences of model misspecification in non-experimental causal inference methods are investigated. The metric is then used to explore which estimators are more sensitive to misspecification of the outcome and/or treatment assignment model. Three frequently used estimators of the treatment effect are considered, all of which rely on the propensity score: (1) full matching, (2) 1:1 nearest neighbor matching, and (3) weighting. The performance of these estimators is evaluated under two different sampling designs: (1) simple random sampling (SRS) and (2) a two-stage stratified survey. As the degree of misspecification of either the propensity score or outcome model increases, so does the bias and the root mean square error, while the coverage decreases. Results are similar for the simple random sample and a complex survey design.

中文翻译：

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测量模型错误指定：在具有复杂调查数据的倾向评分方法中的应用

模型错误指定是任何基于参数模型的分析的潜在问题。然而，在因果推断的背景下，模型错误指定的测量和后果尚未得到很好的形式化。提出了模型错误指定的度量，并研究了非实验因果推理方法中模型错误指定的后果。然后使用该度量来探索哪些估计量对结果和/或治疗分配模型的错误指定更敏感。考虑了三个常用的治疗效果估计量，所有这些估计量都依赖于倾向得分：(1) 完全匹配，(2) 1:1 最近邻匹配，以及 (3) 加权。这些估计器的性能在两种不同的抽样设计下进行评估：(1) 简单随机抽样 (SRS) 和 (2) 两阶段分层调查。随着倾向评分或结果模型的错误指定程度的增加，偏差和均方根误差也会增加，而覆盖率会降低。简单随机样本和复杂调查设计的结果相似。