# Decentralized Causal Discovery using Judo Calculus

arXiv: https://arxiv.org/abs/2510.23942 · HTML: https://mlsanity.com/paper/2510.23942/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 27 Oct 2025.

## Builds on (most cited first, 4 of 4)

- [Differentiable Causal Discovery from Interventional Data](https://mlsanity.com/paper/2007.01754/) (2020)
- [Learning Independent Causal Mechanisms](https://mlsanity.com/paper/1712.00961/) (2017)
- [Intuitionistic j-Do-Calculus in Topos Causal Models](https://mlsanity.com/paper/2510.17944/) (2025)
- [Causal health impacts of power plant emission controls under modeled and uncertain physical process interference](https://mlsanity.com/paper/2306.05665/) (2023)

## Built on by (most cited first, 2 of 2)

- [Large Causal Models from Large Language Models](https://mlsanity.com/paper/2512.07796/) (2025)
- [Latent Confounded Causal Discovery via Lie Bracket Geometry](https://mlsanity.com/paper/2606.19610/) (2026)

## Abstract (arXiv)

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.