发表机构
University College London（伦敦大学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文开发因果基础模型，借助规范先验将反事实界定问题转化为函数分布学习问题，扩展因果基础建模范式以估计未观测混杂下的部分可识别因果效应。
AI 中文摘要
本文研究因果基础模型的开发，该模型用于从观测数据中界定干预和反事实的效应。我们证明，可在具有离散观测变量的结构因果模型空间上定义具有完全支持的规范先验。借助该规范先验，我们将反事实界定问题转化为学习函数分布的问题，这些函数将数据（以及可能的结构假设）映射到感兴趣的因果查询。这将极具前景的因果基础建模范式扩展到部分可识别因果效应的估计，即在未观测混杂的情况下，多个值与观测数据和先验结构假设同等兼容。
英文摘要
This paper investigates the development of causal foundation models for bounding the effect of interventions and counterfactuals from observational data. We show that a canonical prior can be defined with full support over the space of structural causal models with discrete observables. With this canonical prior, we translate the problem of bounding counterfactuals into that of learning distributions over functions that map data (and possibly structural assumptions) to a causal query of interest. This extends the promising causal foundational modelling paradigm to the estimation of partially-identifiable causal effects, i.e., under unobserved confounding, where multiple values are equally compatible with the observed data and prior structural assumptions.
