A cubical formalisation of topos causal models: intervention, forcing, and a contextuality obstruction
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Abstract (arXiv)
Topos causal models recast causal inference inside a topos: a causal world is a presheaf, an intervention is a sub-model named by a characteristic map into the subobject classifier Om, and reasoning is Kripke-Joyal forcing in an intuitionistic internal language. We give the first axiom-free machine-checked account of this 1-topos core, in Cubical Agda over a previously verified probability monad and do-calculus; the framework is otherwise developed on paper, with central claims stated rather than proved. Three of our results go beyond faithful transcription. We exhibit a contextuality obstruction the programme does not treat: pairwise-consistent local causal data with no global model, detected by a degree-one holonomy class. We delimit the claim that interventions are modelled by the subobject classifier: an intervention and an observation name the same subobject, so Om fixes the target of a do-operation but not the operation itself, which is surgery on the kernels --- where, on a confounder, the interventional and observational laws differ. And we settle the modal unit --- inflationarity is derivable from jtop = top and naturality, not a fourth axiom. We also machine-check the classifier of sieves with its classification theorem, the pullback collating local mechanisms, and the Kripke-Joyal forcing clauses. The development assumes no axioms and typechecks under Agda's texttt--safe flag, with the ordered field discharged at mathbbQ; type-level sheafification and a directed do-calculus are future work.
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References · 5
- 2025Intuitionistic j-Do-Calculus in Topos Causal Modelscited by 5
- 2026Unveiling the Structure of Do-Calculus Reasoning via Derivation Graphscited by 3
- 2023The Topology of Causalitycited by 2
- 2024Directed univalence in simplicial homotopy type theorycited by 1
- 2026A cubical formalisation of conditional independence, Bayesian conditioning, and Pearl's d-separation soundnesscited by 1
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