Foundation Models for Partial Causal Identification

2026-08-21

AI Summary

研究通过定义一个具有全支持的规范先验，将从观察数据中界定干预和反事实效应的问题转化为学习函数分布的问题，以估计部分可识别的因果效应。

Abstract

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.

Problem

• causal foundation models
• observational data
• unobserved confounding
• partial identifiability
• counterfactuals

Innovation

• causal foundation models
• canonical prior
• bounding counterfactuals
• partial identifiability
• unobserved confounding