LOAIOct 20, 2025

Intuitionistic $j$-Do-Calculus in Topos Causal Models

arXiv:2510.17944v14 citationsh-index: 2
Originality Incremental advance
AI Analysis

This work addresses causal inference in complex, non-classical settings for researchers in theoretical AI and causality, but it is incremental as it builds on existing topos causal models.

The paper generalizes Pearl's do-calculus to an intuitionistic setting called $j$-stable causal inference within topos causal models, introducing $j$-do-calculus as a sound rule system based on local truth in internal intuitionistic logic. It provides inference rules mirroring Pearl's and proves soundness, with a companion paper planned for data-driven applications.

In this paper, we generalize Pearl's do-calculus to an Intuitionistic setting called $j$-stable causal inference inside a topos of sheaves. Our framework is an elaboration of the recently proposed framework of Topos Causal Models (TCMs), where causal interventions are defined as subobjects. We generalize the original setting of TCM using the Lawvere-Tierney topology on a topos, defined by a modal operator $j$ on the subobject classifier $Ω$. We introduce $j$-do-calculus, where we replace global truth with local truth defined by Kripke-Joyal semantics, and formalize causal reasoning as structure-preserving morphisms that are stable along $j$-covers. $j$-do-calculus is a sound rule system whose premises and conclusions are formulas of the internal Intuitionistic logic of the causal topos. We define $j$-stability for conditional independences and interventional claims as local truth in the internal logic of the causal topos. We give three inference rules that mirror Pearl's insertion/deletion and action/observation exchange, and we prove soundness in the Kripke-Joyal semantics. A companion paper in preparation will describe how to estimate the required entities from data and instantiate $j$-do with standard discovery procedures (e.g., score-based and constraint-based methods), and will include experimental results on how to (i) form data-driven $j$-covers (via regime/section constructions), (ii) compute chartwise conditional independences after graph surgeries, and (iii) glue them to certify the premises of the $j$-do rules in practice

Foundations

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