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Identification and inference with nonignorable missing covariate data.

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This summary is machine-generated.

Identifying models with missing covariate data is challenging when data are missing not at random. A shadow variable improves identification for parametric and semiparametric models, enabling robust estimation.

Keywords:
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Area of Science:

  • Statistics
  • Econometrics
  • Biostatistics

Background:

  • Missing covariate data poses significant challenges in statistical modeling.
  • Identification of parametric and semiparametric models is often compromised when data are missing not at random.
  • Existing methods may fail without strong parametric assumptions or auxiliary information.

Purpose of the Study:

  • To develop a general approach for identifying parametric and semiparametric models with covariates missing not at random.
  • To investigate the role of a 'shadow variable' in facilitating model identification.
  • To extend identification results to generalized linear models with unrestricted missingness processes.

Main Methods:

  • Illustrating identification challenges with examples for missing not at random covariates.
  • Proposing a general framework for model identification using a shadow variable.
  • Developing an inverse probability weighted (IPW) estimator incorporating the shadow variable.
  • Analyzing identification in generalized linear models under various missingness scenarios.

Main Results:

  • Identification is not guaranteed for models with missing not at random covariates without auxiliary information.
  • A fully observed shadow variable, correlated with the missing covariate, broadly enables identification, even in semiparametric models.
  • The outcome model is identified for common generalized linear models when a shadow variable is present and missingness is unrestricted.
  • Counterexamples demonstrate scenarios where identification fails even with a shadow variable.

Conclusions:

  • The use of a shadow variable is crucial for achieving robust identification in models with missing not at random covariates.
  • The proposed IPW estimator offers a practical approach for estimation in these challenging settings.
  • The findings have implications for statistical inference in fields with prevalent missing data issues.