arXiv cs.AI· Tatiana Boura, Robert Peharz·· 12 小时前AI 评分24
面向结构因果模型的半解析推理框架
A Semi-Analytical Inference Framework for Structural Causal Models
AI 导读
研究者提出一种直接在结构因果模型（SCM）上做消息传递的半解析推理框架，将结构机制与概率密度统一表示在有限维函数空间中。前向传播结合 LOTUS 的数值实现，传播密度由样条投影从 LOTUS 矩中恢复，同一表示自然给出用于条件化的反向消息传递。
正文 · 原文
Abstract:Structural Causal Models (SCMs) provide a principled framework for observational, interventional, and counterfactual reasoning, all of which rely on probabilistic inference. Existing frameworks either transform SCMs into alternative tractable representations or rely on approximate inference with expressive generative models. We instead perform inference using a message passing scheme directly on the SCM by representing structural mechanisms and probability densities in a common finite-dimensional function space. Forward propagation combines a numerical realization of the Law of the Unconscious Statistician, while propagated densities are recovered by spline projection from LOTUS moments. The same representation naturally yields backward message passing for conditioning, providing a unified framework for causal inference. Using tensor-product B-spline bases, the resulting algorithm expresses inference through numerical quadrature and linear algebra without explicitly constructing conditional distributions. Experiments on randomly generated continuous SCMs demonstrate accurate approximation of Monte Carlo reference solutions and systematic improvement with increasing basis resolution.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.28103 [cs.AI] |
| (or arXiv:2610.28103v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.28103 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Tatiana Boura [view email]
[v1]
Tue, 20 Oct 2026 12:17:37 UTC (1,742 KB)
来源：arXiv cs.AI · arxiv.org