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Handling Missing Data in Instrumental Variable Methods for Causal Inference.

Edward H Kennedy1, Jacqueline A Mauro1, Michael J Daniels2

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Summary
This summary is machine-generated.

Missing instrument data is common in instrumental variable studies. This review covers methods for causal effect estimation and inference under missing-at-random assumptions, including likelihood-based and doubly robust estimators.

Keywords:
causal inferenceinstrumental variablemissing dataobservational studysemiparametric efficiency

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Genetics

Background:

  • Missing instrument data is a frequent challenge in instrumental variable (IV) studies.
  • Genotype data used as instruments in Mendelian randomization can be missing due to sample collection or ambiguous genotyping output.

Purpose of the Study:

  • To review and discuss various methods for estimating and inferring causal effects in the presence of missing instrument data.
  • To explore different missing-at-random assumptions applicable to instrumental variable analysis.

Main Methods:

  • Review of likelihood-based methods, regression estimators, weighting estimators, and doubly robust estimators.
  • Discussion of asymptotic properties and inferential tools for each method.
  • Implementation of estimators using the Wisconsin Longitudinal Study data.

Main Results:

  • Likelihood-based methods offer the most precise inference under model assumptions.
  • Doubly robust estimators achieve nonparametric efficiency with flexible nuisance function estimation.
  • Regression and weighting estimators are often simpler to implement.

Conclusions:

  • The study provides a comprehensive review of estimators for instrumental variable analysis with missing data.
  • Different methods offer trade-offs between precision, efficiency, and ease of implementation.
  • The findings are applied to investigate the effect of cognitive functioning on depression.