arXiv：cs.LG· Tom\`as Garriga, Valentyn Melnychuk, Konstantin Hess, Eduard Serrahima de Cambra, Axel Brando, Gerard Sanz, Stefan Feuerriegel·· 4 小时前AI 评分33
OrthoGen：面向时变治疗的生成式正交学习器
OrthoGen: A Generative Orthogonal Learner for Time-Varying Treatments
AI 导读
OrthoGen 是一个 Neyman 正交、双重稳健的生成式学习器，用于在时变治疗下估计条件分布潜在结果（CDPO）。它基于新提出的生成式递归 g-computation 调整策略，递归传播完整条件结果分布而非条件均值，并在适当条件下实现速率双重稳健性与准 oracle 效率。
正文
Abstract:Estimating conditional distributional potential outcomes (CDPOs) over time is important in medicine (e.g., to estimate patient-specific risks under different treatment sequences). However, this task is challenging because of time-varying confounding, yet existing adjustment strategies for this task are limited. In this paper, we aim to learn CDPOs under time-varying treatments using flexible generative models. Our contributions are two-fold. (1) We introduce a tailored adjustment strategy for our setting, namely, generative recursive g-computation. Our adjustment strategy recursively propagates full conditional outcome distributions rather than conditional means, modeling the variables of interest directly rather than full trajectories. Building on our adjustment strategy, we formulate simple generative learners for CDPO estimation. However, these learners can be sensitive to nuisance estimation errors, which motivates an orthogonal learner. (2) We thus introduce OrthoGen, a Neyman-orthogonal and doubly robust generative learner. Importantly, we show that OrthoGen further achieves rate double robustness and quasi-oracle efficiency under suitable conditions. Our learners are flexible and can be instantiated with different generative backbones (e.g., normalizing flows and diffusion models). Across experiments with synthetic, semi-synthetic and real-world datasets, we find that OrthoGen is highly effective. To the best of our knowledge, we are the first to propose a generative orthogonal learner for estimating CDPOs under time-varying treatments.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.10210 [cs.LG] |
| (or arXiv:2610.10210v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.10210 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Tomàs Garriga [view email]
[v1]
Wed, 7 Oct 2026 15:07:05 UTC (257 KB)
来源：arXiv：cs.LG · arxiv.org