PICLE-PFN：首个支持动态治疗策略的纵向因果效应基础模型
A Foundation Model for Policy In-Context Longitudinal Causal Effect Estimation
这篇论文搞了个因果效应估计的基础模型 PICLE-PFN，一次前向传播就能出结果，不用每个数据集单独拟合模型，跑得还快。
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。它通过 in-context 历史决策示例来表示目标策略，在从纵向结构因果模型采成的合成数据上预训练，跨数据集、策略和预测时域摊销估计成本。给定新的观测数据集和目标策略，它一次前向传播即可直接预测条件平均潜在结果（CAPOs），无需针对任务单独训练或调参。在合成与半合成纵向基准上，其 RMSE 与最先进传统估计器相当，不确定性校准明显更好，部署运行时间仅为后者的一小部分。
A Foundation Model for Policy In-Context Longitudinal Causal Effect Estimation
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.