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Choosing the right covariates is key for causal inference methods like g-computation. Including all outcome-causing covariates minimizes bias and variance, improving results in real-world applications.

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Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Confounding bias significantly impacts causal inference.
  • Current methods for bias control require careful covariate selection, posing a challenge.

Purpose of the Study:

  • To compare the performance of four causal inference methods (g-computation, inverse probability of treatment weighting, full matching, targeted maximum likelihood estimator) using different covariate sets.
  • To evaluate the impact of covariate selection strategies on bias and variance in causal inference.

Main Methods:

  • Simulation study with binary treatment and outcome, and baseline confounders.
  • Comparison of four covariate sets: outcome-causing, treatment-causing, both, and all covariates.
  • Application of g-computation, inverse probability of treatment weighting, full matching, and targeted maximum likelihood estimator.

Main Results:

  • Including all outcome-causing covariates yielded the lowest bias and variance, especially for g-computation.
  • Considering all covariates did not reduce bias but significantly decreased statistical power.
  • Real-world examples demonstrated the practical importance of these methods.

Conclusions:

  • Optimal covariate selection is crucial for accurate causal inference.
  • The proposed R package RISCA facilitates the use of g-computation.
  • Findings guide the selection of covariates to enhance the reliability of causal effect estimates.