用基础模型估算观测数据中部分可识别的因果效应
Foundation Models for Partial Causal Identification
- 构建覆盖离散可观测变量结构因果模型空间的通用先验
- 将反事实边界问题转化为函数分布学习，适配未观测混杂场景
- 适合研究因果推断不确定性的学者，拓展了因果基础模型应用
本文研究从观测数据中构建用于界定干预和反事实影响的因果基础模型。我们定义了一个在离散可观测变量的结构因果模型空间上具有全支撑的规范先验。基于此先验，将反事实边界问题转化为学习从数据（及可能的结构假设）映射到目标因果查询的函数分布。该方法将有前景的因果基础建模范式拓展至未观测混杂下的部分可识别因果效应估计，即在多个值均与观测数据和先验结构假设相容时仍能提供合理边界。
原文摘要 · Abstract (English)
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.
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