Decentralized Causal Discovery using Judo Calculus
This addresses the need for more robust causal inference in fields like biology and economics by handling regime-specific dependencies, though it builds incrementally on existing causal discovery techniques.
The paper tackles the problem of context-dependent causal discovery in real-world applications by introducing a decentralized framework using judo calculus, which formalizes local truth across regimes and shows computational efficiency and improved performance over classical methods.
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.