analiza aiqbench
This research introduces a sheaf-theoretic framework for causal discovery that improves efficiency and accuracy across varying regimes in biology, medicine, and economics. By formalizing context dependence as local truth within decentralized environments, the method outperforms classical approaches while maintaining constructive consistency.
Decentralized Causal Discovery using Judo Calculus
arXiv:2510.23942v2 Announce Type: replace Abstract: We describe a theory and implementation of an intuitionistic decentralized framework for causal discovery using judo calculus, which is formally defined as j-stable causal inference using j-do-calculus in a topos of sheaves. In real-world applications -- from biology to medicine and social science -- causal effects depend on regime (age, country, dose, genotype, or lab protocol). Our proposed judo calculus formalizes this context dependence formally as local truth: a causal claim is proven true on a cover of regimes, not everywhere at once. The Lawvere-Tierney modal operator j chooses which regimes are relevant; j-stability means the claim holds constructively and consistently across that family. We describe an algorithmic and implementation framework for judo calculus, combining it with standard score-based, constraint-based, and gradient-based causal discovery methods. We describe experimental results on a range of domains, from synthetic to real-world datasets from biology and economics. Our experimental results show the computational efficiency gained by the decentralized nature of sheaf-theoretic causal discovery, as well as improved performance over classical causal discovery methods.
Abstract:We describe a theory and implementation of an intuitionistic decentralized framework for causal discovery using judo calculus, which is formally defined as j-stable causal inference using j-do-calculus in a topos of sheaves. In real-world applications -- from biology to medicine and social science -- causal effects depend on regime (age, country, dose, genotype, or lab protocol). Our proposed judo calculus formalizes this context dependence formally as local truth: a causal claim is proven true on a cover of regimes, not everywhere at once. The Lawvere-Tierney modal operator j chooses which regimes are relevant; j-stability means the claim holds constructively and consistently across that family. We describe an algorithmic and implementation framework for judo calculus, combining it with standard score-based, constraint-based, and gradient-based causal discovery methods. We describe experimental results on a range of domains, from synthetic to real-world datasets from biology and economics. Our experimental results show the computational efficiency gained by the decentralized nature of sheaf-theoretic causal discovery, as well as improved performance over classical causal discovery methods.
Substantially revised and shortened version. We refocus the paper on decentralized multi-environment causal discovery and its experiments, and relegate the theory to the companion revised j-do-calculus paper. A detailed revision log is included in the paper
Artificial Intelligence (cs.AI)
arXiv:2510.23942 [cs.AI]
(or arXiv:2510.23942v2 [cs.AI] for this version)
Text integral sub licența deschisă a publicației · 216 cuvinte
Subiecte conexe
The Next Swan: Frank Ramsey, Variable Hypotheticals, and the Bet on Induction
LessWrong · 8 iun. 2026
Intuitionistic $j$-Do-Calculus in Topos Causal Models
arXiv · 28 iul. 2026
[AINews] OpenAI shuts off Cursor
Latent Space · 29 aug. 2026
The Critical Role of Model Selection in Causal Inference: A Comparative Analysis of Classification Models within the InferBERT Framework for Pharmacovigilance
arXiv · 17 iun. 2026