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arXiv 提交日期: 2026-06-23
📄 Abstract - Infinitesimal Causality

This paper introduces a categorical account of infinitesimal causality in Frobenius Markov categories equipped with tangent-bundle semantics. IDC captures the infinitesimal layer in which interventions act as tangent deformations of copy/discard structure. Two distinct Frobenius structures interact: (1) the categorical Frobenius algebra on classical variables encoding copying, comparing, and discarding; and (2) the geometric Frobenius integrability condition, namely involutive closure of the intervention distribution, distinct from the algebraic Frobenius structure. Categorical causal sufficiency is defined as the compatibility of these two notions. A key observation is that, for structural causal models, infinitesimal causality is most naturally formulated in the slice of deterministic mechanisms over exogenous variables, with visible stochastic kernels obtained only after pushforward. Interventions are tangent vectors that deform the Frobenius copy/discard operations; their Lie brackets measure whether this deformation preserves classical information-flow structure. Pearl's do-calculus is used as a guiding example of intervention identities: ignoring irrelevant interventions corresponds to counit invariance, action/observation exchange to coproduct compatibility with pushforward, and independence to involutive bracket closure of the visible intervention distribution.

顶级标签: causality machine learning
详细标签: causal inference frobenius markov tangent bundle do-calculus categorical model 或 搜索:

无穷小因果性 / Infinitesimal Causality


1️⃣ 一句话总结

本文在配备切丛结构的Frobenius Markov范畴中提出了一种描述无穷小因果关系的范畴化方法,将干预解释为对复制/丢弃结构的切形变,并通过两种Frobenius结构的兼容性定义了因果充分性,最终用Pearl的do-calculus验证了该框架的合理性。

源自 arXiv: 2606.24621