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This study introduces a non-Gaussian forward selection (nGFS) method for selecting control variables in observational research. The nGFS algorithm effectively identifies crucial covariates, improving causal effect estimation, especially with large sample sizes and non-Gaussian data.

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Area of Science:

  • Behavioral science
  • Developmental science
  • Social science

Background:

  • Causal mechanism understanding is key in social and behavioral sciences.
  • Covariate adjustment is vital for estimating causal effects from observational data.
  • Selecting appropriate covariates is challenging, as mere availability is insufficient.

Purpose of the Study:

  • Introduce a novel non-Gaussian method for covariate selection.
  • Develop a forward selection algorithm (nGFS) for linear models.
  • Enhance the accuracy of causal effect estimation by avoiding bias and inconsistency.

Main Methods:

  • Proposed a non-Gaussian forward selection (nGFS) algorithm.
  • Applied the algorithm to linear models for covariate selection.
  • Utilized a Monte Carlo simulation study to evaluate performance.

Main Results:

  • The nGFS algorithm performs well, particularly with large sample sizes (n ≥ 250).
  • Performance is enhanced when data significantly deviate from Gaussianity (skewness > 1.5).
  • The algorithm aligns with principles of direction of dependence for causal model specification.

Conclusions:

  • The nGFS method offers a robust approach to covariate selection in observational studies.
  • This method improves the reliability of causal effect estimates.
  • It is particularly effective for non-Gaussian data and larger datasets.