发表机构
Novartis; Barcelona Supercomputing Center; LMU Munich; Munich Center for Machine Learning; Universitat Politècnica de Catalunya（诺华; 巴塞罗那超级计算中心; 慕尼黑大学; 慕尼黑机器学习中心; 加泰罗尼亚理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出OrthoGen，一种用于时变治疗下条件分布潜在结果估计的生成式正交学习器，通过生成式递归g计算和Neyman正交性实现双重稳健性，并在多种数据集上验证其有效性。
AI 中文摘要
在医学等领域，随时间估计条件分布潜在结果（CDPO）具有重要意义（例如，估计不同治疗序列下患者特定风险）。然而，由于时变混杂的存在，这一任务具有挑战性，且现有针对该任务的调整策略有限。本文旨在利用灵活的生成模型学习时变治疗下的CDPO。我们的贡献有两方面：（1）我们针对我们的设置引入了一种定制调整策略，即生成式递归g计算。我们的调整策略递归传播完整的条件结果分布而非条件均值，直接对感兴趣的变量建模而非完整轨迹。基于我们的调整策略，我们提出了用于CDPO估计的简单生成学习器。然而，这些学习器可能对干扰估计误差敏感，这促使我们设计正交学习器。（2）因此，我们引入了OrthoGen，一种Neyman正交且双重稳健的生成学习器。重要的是，我们证明了OrthoGen在适当条件下进一步实现了速率双重稳健性和准有效效率。我们的学习器是灵活的，可以用不同的生成骨干（如归一化流和扩散模型）实例化。在合成、半合成和真实世界数据集的实验中，我们发现OrthoGen非常有效。据我们所知，我们是第一个提出用于估计时变治疗下CDPO的生成式正交学习器。
英文摘要
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.
