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Multiply robust estimation of causal quantile treatment effects.

Yuying Xie1, Cecilia Cotton2, Yeying Zhu2

  • 1Biometrics Department, Hoffmann-La Roche Limited, Mississauga, Ontario, Canada.

Statistics in Medicine
|August 29, 2020
PubMed
Summary

This study introduces a robust statistical method for estimating quantile treatment effects, offering reliable results even with model inaccuracies. The approach enhances causal inference by balancing key outcome and propensity score distributions.

Keywords:
causal inferencecovariate balancingempirical likelihoodmultiple robustnessquantile treatment effect

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Area of Science:

  • Statistics
  • Econometrics
  • Epidemiology

Background:

  • Causal inference often focuses on average causal effects.
  • Quantile treatment effects provide deeper insights into treatment outcome distributions.

Purpose of the Study:

  • To propose a multiply robust method for estimating marginal quantiles of potential outcomes.
  • To offer an alternative to inverse probability weighting that is less sensitive to model misspecification.

Main Methods:

  • Achieving mean balance in propensity scores and conditional distributions of potential outcomes.
  • Utilizing empirical likelihood or entropy measure for estimation.
  • Conducting simulation studies under various model specification scenarios.

Main Results:

  • The proposed estimator is consistent if at least one of the models (propensity score or outcome) is correctly specified.
  • Empirical likelihood/entropy methods offer robustness against propensity score model misspecification.
  • Demonstrated consistency and robustness through simulations and theoretical analysis.

Conclusions:

  • The developed method provides a consistent and robust approach to estimating quantile treatment effects.
  • This technique is valuable for analyzing complex causal relationships in observational studies.
  • Applied to investigate the effect of maternal smoking on infant birthweight, demonstrating practical utility.