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Subclassification estimation of the weighted average treatment effect.

Byeong Yeob Choi1

  • 1Department of Population Health Sciences, University of Texas Health San Antonio, San Antonio, TX, USA.

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|July 16, 2021
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Summary

This study introduces generalized stratum weights for subclassification, enhancing causal effect estimation. The proposed method offers improved robustness against model misspecification for the average treatment effect in the overlap population.

Keywords:
augmented subclassificationgeneralized stratum weightsoverlap weightspropensity scoressubclassificationweighted average treatment effects

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Area of Science:

  • Causal inference
  • Statistical modeling
  • Epidemiology

Background:

  • Propensity scores (PSs) are widely used for causal effect estimation via weighting and subclassification.
  • Weighting offers consistent estimators but is sensitive to PS model misspecification.
  • Subclassification is more robust but has limited application to diverse causal estimands.

Purpose of the Study:

  • To propose generalized stratum weights for implementing subclassification estimators for various causal estimands.
  • To extend subclassification to estimate the average treatment effect for the overlap population (ATO).
  • To develop augmented subclassification estimators for improved bias reduction.

Main Methods:

  • Incorporation of strata into the weighted average treatment effect (WATE) expression.
  • Identification of stratum weights for ATO, equivalent to optimal stratum weights (inverse variances).
  • Development of augmented subclassification estimators.

Main Results:

  • The proposed subclassification estimator for ATO demonstrates greater robustness to model misspecification than the weighting estimator.
  • Augmented subclassification estimators show reduced bias when only the outcome model is correctly specified.
  • The methods were illustrated using a study on right heart catheterization.

Conclusions:

  • Generalized stratum weights effectively extend subclassification for diverse causal estimands, including ATO.
  • The proposed subclassification approach offers a robust alternative to weighting, particularly under model misspecification.
  • Augmented estimators provide further improvements in bias reduction.