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Regression with race-modifiers: towards equity and interpretability.

Daniel R Kowal1

  • 1Department of Statistics, Rice University, Houston, TX 77005.

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Summary
This summary is machine-generated.

Structural racism biases statistical analysis, but abundance-based constraints (ABCs) eliminate this racial bias. This method allows for unbiased estimation of race-specific effects without sacrificing accuracy or efficiency.

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Area of Science:

  • Quantitative Social Science
  • Biostatistics
  • Health Disparities Research

Background:

  • Structural racism and racial discrimination significantly impact health and life outcomes, with effects often varying by race.
  • Standard statistical regression methods can introduce and perpetuate racial biases in the analysis and presentation of race-modified effects.

Conclusions:

  • Abundance-based constraints offer a powerful tool to mitigate racial bias in quantitative research, particularly in studies examining health and social disparities.
  • This approach enhances the accuracy and equity of statistical inference, allowing for a more nuanced understanding of race-modified effects.
  • The findings underscore the importance of addressing structural racism in statistical practices to achieve more equitable research outcomes.