A Foundation Model for Policy In-Context Longitudinal Causal Effect Estimation· 动态治疗政策下的纵向因果效应估计基础模型
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 mo
提出动态治疗政策下的纵向因果效应估计基础模型 PICLE-PFN。
- 核心方法
- PICLE-PFN 通过上下文历史决策示例表示目标政策，支持广泛的动态治疗策略，无需针对具体数据拟合烦琐模型。
- 适合谁读
- 研究者、工程师
- 要解决的问题
- 现有方法难以处理时间变化的混杂因素及治疗-混杂反馈循环，且计算耗时较长。
- 关键实验
- 未提供
- 主要贡献
- 首次提出支持动态治疗政策的纵向因果效应估计基础模型，提供统一接口，减少计算耗时。
- 意义与局限
- 该模型有助于更高效、灵活地估计复杂动态环境下的因果效应，促进个性化治疗决策。其局限性在于目前仅基于合成数据训练，实际应用的有效性有待验证。