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Title: Resampling‐based confidence intervals for model‐free robust inference on optimal treatment regimes
Abstract

We propose a new procedure for inference on optimal treatment regimes in the model‐free setting, which does not require to specify an outcome regression model. Existing model‐free estimators for optimal treatment regimes are usually not suitable for the purpose of inference, because they either have nonstandard asymptotic distributions or do not necessarily guarantee consistent estimation of the parameter indexing the Bayes rule due to the use of surrogate loss. We first study a smoothed robust estimator that directly targets the parameter corresponding to the Bayes decision rule for optimal treatment regimes estimation. This estimator is shown to have an asymptotic normal distribution. Furthermore, we verify that a resampling procedure provides asymptotically accurate inference for both the parameter indexing the optimal treatment regime and the optimal value function. A new algorithm is developed to calculate the proposed estimator with substantially improved speed and stability. Numerical results demonstrate the satisfactory performance of the new methods.

 
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Award ID(s):
1952373
NSF-PAR ID:
10450901
Author(s) / Creator(s):
 ;  
Publisher / Repository:
Oxford University Press
Date Published:
Journal Name:
Biometrics
Volume:
77
Issue:
2
ISSN:
0006-341X
Format(s):
Medium: X Size: p. 465-476
Size(s):
["p. 465-476"]
Sponsoring Org:
National Science Foundation
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