从类型到 Token：Petri 网、个体 Token 哲学与个体化因果效应
From Type to Token: Petri Nets, Individual Token Philosophy, and Individualized Causal Effects
AI 导读
arXiv 论文提出，结构因果模型与贝叶斯网络属于类型层面框架，其 Average Treatment Effects 等估计量在处理效应呈现无界异质性时会失效。作者主张在个体 Token 哲学下，Petri 网是 Token 级因果结构的自然表示，并给出从贝叶斯网络到 Petri 网的形式化翻译。配套的 conformal prediction 层以有限样本覆盖保证量化个体因果效应。
正文 · 原文
Abstract:Structural Causal Models and Bayesian Networks are the dominant framework for representing causal relations. They are fundamentally type-level formalisms since their estimands, Average Treatment Effects and Conditional Average Treatment Effects, are properties of distributions over populations, not of individual units. When treatment effects exhibit unbounded heterogeneity, these estimands collapse. What persists is the structural, token-level core of causation.
We argue that Petri Nets under the Individual Token Philosophy are the natural representation of these token-level causal structures. We develop a formal translation from Bayesian Networks to Petri Nets, providing a two-layer account of individualized causality. The PN layer provides a structural representation of causal mechanisms that does not rest on distributional assumptions. In turn, a conformal prediction layer quantifies causal effects with finite-sample coverage guarantees. Together they constitute a complete account of individual causal effects.
| Subjects: | Artificial Intelligence (cs.AI); Machine Learning (stat.ML) |
| Cite as: | arXiv:2611.05703 [cs.AI] |
| (or arXiv:2611.05703v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.05703 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Fernando Delbianco Dr. [view email]
[v1]
Tue, 3 Nov 2026 22:31:10 UTC (22 KB)
来源：arXiv cs.AI · arxiv.org