Decentralized Causal Discovery using Judo Calculus

• URL: http://arxiv.org/abs/2510.23942v1
• Date: Mon, 27 Oct 2025 23:49:50 GMT
• Title: Decentralized Causal Discovery using Judo Calculus
• Authors: Sridhar Mahadevan,
• Abstract summary: We describe an intuitionistic decentralized framework for causal discovery using judo calculus.<n>A causal claim is proven true on a cover of regimes, not everywhere at once.<n>We show experimental results on a range of domains, from synthetic to real-world datasets from biology and economics.
• Score: 1.3295383263113112
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• HOLOGRAPH: Active Causal Discovery via Sheaf-Theoretic Alignment of Large Language Model Priors [12.969042037563971]
  HOLOGRAPH is a framework that formalizes Large Language Models-guided causal discovery.<n>Our key insight is that coherent global causal structure corresponds to the existence of a global section.<n> Experiments on synthetic and real-world benchmarks demonstrate that HOLOGRAPH provides rigorous mathematical foundations.
  arXiv  Detail & Related papers  (2025-12-30T21:47:05Z)
• The Robustness of Differentiable Causal Discovery in Misspecified Scenarios [18.797446049830636]
  Causal discovery aims to learn causal relationships between variables from targeted data.<n>We show that differentiable causal discovery methods exhibit robustness under the metrics of Structural Hamming Distance and Structural Intervention Distance.
  arXiv  Detail & Related papers  (2025-10-14T13:33:06Z)
• Probably Approximately Correct Causal Discovery [21.260465708905585]
  We propose the Probably Approximately Correct Causal (PACC) Discovery framework.<n>This framework emphasizes both computational and sample efficiency for established causal methods.<n>It can also provide theoretical guarantees for other widely used methods.
  arXiv  Detail & Related papers  (2025-07-25T02:51:15Z)
• Counterfactual Realizability [52.85109506684737]
  We introduce a formal definition of realizability, the ability to draw samples from a distribution, and then develop a complete algorithm to determine whether an arbitrary counterfactual distribution is realizable.<n>We illustrate the implications of this new framework for counterfactual data collection using motivating examples from causal fairness and causal reinforcement learning.
  arXiv  Detail & Related papers  (2025-03-14T20:54:27Z)
• Argumentative Causal Discovery [13.853426822028975]
  Causal discovery amounts to unearthing causal relationships amongst features in data. We deploy assumption-based argumentation (ABA) to learn graphs which reflect causal dependencies in the data. We prove that our method exhibits desirable properties, notably that, under natural conditions, it can retrieve ground-truth causal graphs.
  arXiv  Detail & Related papers  (2024-05-18T10:34:34Z)
• Causal Temporal Regime Structure Learning [49.77103348208835]
  We present CASTOR, a novel method that concurrently learns the Directed Acyclic Graph (DAG) for each regime.<n>We establish the identifiability of the regimes and DAGs within our framework.<n>Experiments show that CASTOR consistently outperforms existing causal discovery models.
  arXiv  Detail & Related papers  (2023-11-02T17:26:49Z)
• Symbolic Recovery of Differential Equations: The Identifiability Problem [52.158782751264205]
  Symbolic recovery of differential equations is the ambitious attempt at automating the derivation of governing equations. We provide both necessary and sufficient conditions for a function to uniquely determine the corresponding differential equation. We then use our results to devise numerical algorithms aiming to determine whether a function solves a differential equation uniquely.
  arXiv  Detail & Related papers  (2022-10-15T17:32:49Z)
• BaCaDI: Bayesian Causal Discovery with Unknown Interventions [118.93754590721173]
  BaCaDI operates in the continuous space of latent probabilistic representations of both causal structures and interventions. In experiments on synthetic causal discovery tasks and simulated gene-expression data, BaCaDI outperforms related methods in identifying causal structures and intervention targets.
  arXiv  Detail & Related papers  (2022-06-03T16:25:48Z)
• Counterfactual Fairness with Partially Known Causal Graph [85.15766086381352]
  This paper proposes a general method to achieve the notion of counterfactual fairness when the true causal graph is unknown. We find that counterfactual fairness can be achieved as if the true causal graph were fully known, when specific background knowledge is provided.
  arXiv  Detail & Related papers  (2022-05-27T13:40:50Z)
• Principled Knowledge Extrapolation with GANs [92.62635018136476]
  We study counterfactual synthesis from a new perspective of knowledge extrapolation. We show that an adversarial game with a closed-form discriminator can be used to address the knowledge extrapolation problem. Our method enjoys both elegant theoretical guarantees and superior performance in many scenarios.
  arXiv  Detail & Related papers  (2022-05-21T08:39:42Z)
• Causal Expectation-Maximisation [70.45873402967297]
  We show that causal inference is NP-hard even in models characterised by polytree-shaped graphs. We introduce the causal EM algorithm to reconstruct the uncertainty about the latent variables from data about categorical manifest variables. We argue that there appears to be an unnoticed limitation to the trending idea that counterfactual bounds can often be computed without knowledge of the structural equations.
  arXiv  Detail & Related papers  (2020-11-04T10:25:13Z)
• Causal Relational Learning [29.082088734252213]
  We propose a declarative language called CaRL for capturing causal background knowledge and assumptions. CaRL provides a foundation for inferring causality and reasoning about the effect of complex interventions in relational domains.
  arXiv  Detail & Related papers  (2020-04-07T18:33:05Z)
• On Computation and Generalization of Generative Adversarial Imitation Learning [134.17122587138897]
  Generative Adversarial Learning (GAIL) is a powerful and practical approach for learning sequential decision-making policies. This paper investigates the theoretical properties of GAIL.
  arXiv  Detail & Related papers  (2020-01-09T00:40:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.