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Estimating average treatment effects with a double-index propensity score.

David Cheng1, Abhishek Chakrabortty2, Ashwin N Ananthakrishnan3

  • 1VA Boston Healthcare System, Boston, Massachusetts.

Biometrics
|December 5, 2019
PubMed
Summary

This study introduces a new method for estimating treatment effects using observational data, improving accuracy when many variables are involved. The Double-index Propensity Score (DiPS) enhances robustness and efficiency in statistical analysis.

Keywords:
causal inferencedouble-robustnesselectronic medical recordskernel smoothingregularizationsemiparametric efficiency

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Statistical Learning

Background:

  • Estimating average treatment effects (ATE) in observational studies often requires selecting relevant covariates from a large set.
  • Traditional methods may struggle with high-dimensional covariates and potential model misspecification.

Purpose of the Study:

  • To develop a robust and efficient method for estimating ATE in observational data with high-dimensional covariates.
  • To introduce a novel propensity score (PS) estimator that improves upon existing doubly robust methods.

Main Methods:

  • Proposed the Double-index Propensity Score (DiPS) estimator, smoothing treatment status over predictors from working PS and outcome models.
  • Utilized adaptive LASSO for dimension reduction and regularization in fitting parametric PS models.
  • Employed normalized inverse probability weighting with the DiPS estimator.

Main Results:

  • The DiPS estimator maintains double robustness and local semiparametric efficiency.
  • The smoothing approach enhances efficiency and robustness, especially under working model misspecification.
  • The method performs well in simulations and is demonstrated in real-world medical studies.

Conclusions:

  • The DiPS method offers a robust and efficient approach for estimating ATE with high-dimensional covariates in observational studies.
  • This technique provides advantages over traditional doubly robust estimators, particularly when models are misspecified.
  • The study successfully applies the method to estimate the effects of statins and smoking in medical record and cohort studies.