A Cp criterion for semiparametric causal inference

• 成果类型: Article
• 署名作者: Baba, Takamichi; Kanemori, Takayuki; Ninomiya, Yoshiyuki
• 署名单位: Shionogi & Company Limited; Kyushu University
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asx054
• 发表日期: 2017
• 页码: 845861
• 关键词: MARGINAL STRUCTURAL MODELS; doubly robust estimation; information criterion; propensity score; Missing Data; adjustment; selection

摘要

For marginal structural models, which play an important role in causal inference, we consider a model selection problem within a semiparametric framework using inverse-probability-weighted estimation or doubly robust estimation. In this framework, the modelling target is a potential outcome that may be missing, so there is no classical information criterion. We define a mean squared error for treating the potential outcome and derive an asymptotic unbiased estimator as a criterion using an ignorable treatment assignment condition. Simulation shows that the proposed criterion outperforms a conventional one by providing smaller squared errors and higher frequencies of selecting the true model in all the settings considered. Moreover, in a real-data analysis we found a clear difference between the two criteria.