April 2024 High-dimensional inference for dynamic treatment effects
Jelena Bradic, Weijie Ji, Yuqian Zhang
Author Affiliations +
Ann. Statist. 52(2): 415-440 (April 2024). DOI: 10.1214/24-AOS2352


Estimating dynamic treatment effects is a crucial endeavor in causal inference, particularly when confronted with high-dimensional confounders. Doubly robust (DR) approaches have emerged as promising tools for estimating treatment effects due to their flexibility. However, we showcase that the traditional DR approaches that only focus on the DR representation of the expected outcomes may fall short of delivering optimal results. In this paper, we propose a novel DR representation for intermediate conditional outcome models that leads to superior robustness guarantees. The proposed method achieves consistency even with high-dimensional confounders, as long as at least one nuisance function is appropriately parametrized for each exposure time and treatment path. Our results represent a significant step forward as they provide faster convergence rates and new robustness guarantees. The key to achieving these results lies in utilizing DR representations for intermediate conditional outcome models, which offer superior inferential performance while requiring weaker assumptions. Lastly, we examine finite sample behavior through simulations and a real data application.

Funding Statement

This work was supported in part by NSF awards CNS-1730158, ACI-1540112, ACI-1541349, OAC-1826967, the University of California Office of the President and the University of California San Diego’s California Institute for Telecommunications and Information Technology/Qualcomm Institute.
Jelena Bradic’s work has been supported by the NSF Grant DMS-1712481.
Yuqian Zhang’s work has been supported by the National Natural Science Foundation of China (NSFC) grant 12301390.
The majority of this work was done while Yuqian Zhang and Weijie Ji were with the Department of Mathematics, University of California San Diego.


Download Citation

Jelena Bradic. Weijie Ji. Yuqian Zhang. "High-dimensional inference for dynamic treatment effects." Ann. Statist. 52 (2) 415 - 440, April 2024. https://doi.org/10.1214/24-AOS2352


Received: 1 July 2022; Revised: 1 December 2023; Published: April 2024
First available in Project Euclid: 9 May 2024

Digital Object Identifier: 10.1214/24-AOS2352

Primary: 62C99 , 62G35
Secondary: 62J07

Keywords: Double-robustness , imputed Lasso , missing data , model misspecification

Rights: Copyright © 2024 Institute of Mathematical Statistics


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Vol.52 • No. 2 • April 2024
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