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Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
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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
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.
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.
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