arXiv cs.AI· Zeqin Yang, Weilin Chen, Ruichu Cai, Yuguang Yan, Zhifeng Hao, Zhipeng Yu, Zhichao Zou, Jixing Xu, Zhen Peng, Jiecheng Guo·· 14 小时前AI 评分18
利用最优传输权重估计长期异质剂量反应曲线及其泛化界
Estimating Long-term Heterogeneous Dose-response Curve: Generalization Bound Leveraging Optimal Transport Weights
AI 导读
研究提出一种在存在未观测混杂因素和连续处理条件下估计长期异质剂量反应曲线（HDRC）的方法，通过最优传输加权框架将长期观测数据与辅助短期实验数据对齐。基于重加权分布建立了反事实预测误差的泛化界，并据此构建长期 HDRC 估计器。合成与半合成数据集实验验证了该方法的有效性。
正文 · 原文
Abstract:Long-term treatment effect estimation is a significant but challenging problem in many applications. Existing methods rely on ideal assumptions, such as no unobserved confounders or binary treatment, to estimate long-term average treatment effects. However, in numerous real-world applications, these assumptions could be violated, and average treatment effects are insufficient for personalized decision-making. In this paper, we address a more general problem of estimating long-term Heterogeneous Dose-Response Curve (HDRC) while accounting for unobserved confounders and continuous treatment. Specifically, to remove the unobserved confounders in the long-term observational data, we introduce an optimal transport weighting framework to align the long-term observational data to an auxiliary short-term experimental data. Furthermore, to accurately predict the heterogeneous effects of continuous treatment, we establish a generalization bound on counterfactual prediction error by leveraging the reweighted distribution induced by optimal transport. Finally, we develop a long-term HDRC estimator building upon the above theoretical foundations. Extensive experiments on synthetic and semi-synthetic datasets demonstrate the effectiveness of our approach.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2406.19195 [cs.LG] |
| (or arXiv:2406.19195v4 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2406.19195 arXiv-issued DOI via DataCite |
Submission history
From: Zeqin Yang [view email]
[v1]
Thu, 27 Jun 2024 14:13:46 UTC (1,304 KB)
[v2]
Fri, 16 May 2025 07:28:31 UTC (1,319 KB)
[v3]
Wed, 22 Oct 2025 13:52:31 UTC (1,131 KB)
[v4]
Mon, 26 Oct 2026 09:12:07 UTC (1,168 KB)
来源：arXiv cs.AI · arxiv.org