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Imputation approaches for potential outcomes in causal inference.

Daniel Westreich1, Jessie K Edwards2, Stephen R Cole2

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International Journal of Epidemiology
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Causal inference is fundamentally a missing data problem. Viewing it through the lens of missing data, using methods like multiple imputation and the parametric g-formula, offers new insights for epidemiologists.

Keywords:
Causal inferenceg-formulamultiple imputationpotential outcomes

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Causal Inference

Background:

  • Causal inference is fundamentally a missing data problem, specifically concerning missing potential outcomes.
  • This connection is often underexplored in epidemiological literature but offers valuable intuition.

Purpose of the Study:

  • To demonstrate novel approaches to causal inference by framing it as a missing data problem.
  • To illustrate the application of multiple imputation and the parametric g-formula in causal inference.

Main Methods:

  • Utilizing multiple imputation techniques to handle missing potential outcomes.
  • Applying the parametric g-formula for causal inference in the presence of missing data.

Main Results:

  • Demonstrated the practical application of multiple imputation and the parametric g-formula using example data.
  • Discussed the implications of these missing data approaches for traditional causal inference methods.

Conclusions:

  • Framing causal inference as a missing data problem provides clarifying insights for epidemiological analyses.
  • Both multiple imputation and g-formula offer distinct advantages and disadvantages for causal inference.