mlsanityData to 7 Oct 2026

Intuitionistic j-Do-Calculus in Topos Causal Models

arXiv 2510.17944 ↗First posted 20 Oct 2025cs.LO cs.AIcited by 4

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Abstract (arXiv)

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 Omega. 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

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 7 of its 28 references that are in the map. First posted on arXiv 20 Oct 2025.

Builds on

7 of 28 references.

References · 7

  1. 2017Learning Independent Causal Mechanismscited by 104
  2. 2019A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statisticscited by 55
  3. 2022Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Datacited by 42
  4. 2017Disintegration and Bayesian Inversion via String Diagramscited by 32
  5. 2017Markov Properties for Graphical Models with Cycles and Latent Variablescited by 32
  6. 2018Causal Inference by String Diagram Surgerycited by 23
  7. 2022The d-separation criterion in Categorical Probabilitycited by 12

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

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  1. 2022Markov categories, causal theories, and the do-calculus
  2. 2026Uncovering Hidden Correctness in LLM Causal Reasoning via Symbolic Verification
  3. 2023Axiomatizations of causal reasoning under indeterministic causal laws
  4. 2025A Non-Interventionist Approach to Causal Reasoning based on Lewisian Counterfactuals
  5. 2025Gateways to Tractability for Satisfiability in Pearl's Causal Hierarchy
  6. 2023Causal models in string diagrams
  7. 2024On Probabilistic and Causal Reasoning with Summation Operators
  8. 2026Determinization in Structure Theories: A Unified Framework via Closure, Comparability, and Joint Admissibility