arXiv cs.AI· Wenxin Chen, Kyra Gan, Mingyu Lu, Zilin Jing, Fei Wang, Hoifung Poon, Tristan Naumann, Peniel Argaw·· 12 小时前AI 评分31
PICLE-PFN：首个支持动态治疗策略的纵向因果效应估计基础模型
A Foundation Model for Policy In-Context Longitudinal Causal Effect Estimation
AI 导读
PICLE-PFN 是首个面向纵向因果效应估计的基础模型，支持动态治疗策略，通过上下文中的历史决策演示统一表示目标策略。该模型在纵向结构因果模型生成的合成数据上预训练，面对新数据集与目标策略仅需一次前向传播即可直接预测条件平均潜在结果（CAPOs），无需任务特定训练或超参数调优。
正文 · 原文
Abstract:Longitudinal causal effect estimation is complicated by time-varying confounding and treatment-confounder feedback loops. Existing methods generally require data-specific nuisance-model fitting, and many are tailored to prespecified treatment sequences or prediction horizons, leading to time-consuming computation at deployment. We introduce *Policy In-Context Longitudinal causal Effect estimation with Prior-data Fitted Networks* (**PICLE-PFN**), the first foundation model for longitudinal causal effect estimation that supports *dynamic* treatment policies. PICLE-PFN represents target policies through in-context history-decision demonstrations, providing a unified interface for a broad class of treatment policies. Pretrained over synthetic data sampled from longitudinal structural causal models, PICLE-PFN amortizes causal effect estimation across datasets, policies, and horizons. Given a new observational dataset and target policy, it directly predicts conditional average potential outcomes (CAPOs) in one forward pass without task-specific training or hyperparameter tuning. Across synthetic and semi-synthetic longitudinal benchmarks, PICLE-PFN achieves competitive RMSE and substantially improved uncertainty calibration with a fraction of deployment runtime, compared to state-of-the-art conventional estimators trained and tuned separately for each dataset.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.32863 [cs.AI] |
| (or arXiv:2610.32863v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.32863 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Wenxin Chen [view email]
[v1]
Fri, 23 Oct 2026 16:34:49 UTC (2,643 KB)
来源：arXiv cs.AI · arxiv.org