ACp criterion for semiparametric causal inference

Takamichi Baba, Takayuki Kanemori, Yoshiyuki Ninomiya

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

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 Cp 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.

Original languageEnglish
Pages (from-to)845-861
Number of pages17
JournalBiometrika
Volume104
Issue number4
DOIs
Publication statusPublished - Dec 1 2017

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Mathematics(all)
  • Agricultural and Biological Sciences (miscellaneous)
  • Agricultural and Biological Sciences(all)
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

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    Baba, T., Kanemori, T., & Ninomiya, Y. (2017). ACp criterion for semiparametric causal inference. Biometrika, 104(4), 845-861. https://doi.org/10.1093/biomet/asx054