HIGH-DIMENSIONAL INFERENCE FOR DYNAMIC TREATMENT EFFECTS

• 成果类型: Article
• 署名作者: Bradic, Jelena; Ji, Weijie; Zhang, Yuqian
• 署名单位: University of California System; University of California San Diego; University of California System; University of California San Diego; Shanghai University of Finance & Economics; Renmin University of China
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/24-AOS2352
• 发表日期: 2024
• 页码: 415-440
• 关键词: robust estimation; Causal Inference; models; FRAMEWORK; wages

摘要

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.