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Joint sufficient dimension reduction and estimation of conditional and average treatment effects
Ming-Yueh Huang1, Kwun Chuen Gary Chan1
1Department of Biostatistics, University of Washington, Seattle, Washington 98105, U.S.A.myh0728@uw.edukcgchan@u.washington.edu.
Biometrika
|February 13, 2018
Summary
This study introduces a new method for estimating treatment effects from observational data by reducing complex confounders to a central subspace. This approach enhances the accuracy and efficiency of treatment effect estimation in large datasets.
Area of Science:
- Statistics
- Biostatistics
- Epidemiology
Background:
- Estimating treatment effects from observational data often involves numerous confounders, necessitating dimension reduction.
- Existing methods may not efficiently handle high-dimensional confounding variables.
Purpose of the Study:
- To define and estimate a central subspace for efficient average treatment effect (ATE) estimation.
- To develop a method for simultaneously estimating structural dimension, central subspace basis, and optimal bandwidth.
Main Methods:
- Proposed a criterion for simultaneous estimation of structural dimension and joint central subspace basis.
- Utilized forward selection for easy implementation.
- Employed semiparametric efficient estimation of ATE using data-adaptive bandwidth for optimal undersmoothing.
Main Results:
- Successfully reduced covariate dimension from 11 to an effective dimension of one in a nutritional study.
- Demonstrated asymptotic properties of the estimated joint central subspace and ATE estimator.
- The proposed method allows for efficient estimation of treatment effects in the presence of high-dimensional confounders.
Conclusions:
- The developed method provides an effective approach for dimension reduction in observational studies.
- This facilitates more accurate and efficient estimation of average treatment effects.
- The approach is applicable to various fields, including nutritional epidemiology.
Keywords:
Forward selectionHigh-order kernelJoint central subspaceOptimal bandwidthSemiparametric efficiencyUndersmoothingMore Related Videos
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