A Calculus for Causal Relevance

2013/01/10 by Blai Bonet, Bonet, Blai
Computer Science · #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Logic, Reasoning, and Knowledge #Rough Sets and Fuzzy Logic

paper · pdf · doi:10.48550/arxiv.1301.2257

openalex publication_date 2013/01/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper presents a sound and completecalculus for causal relevance, based onPearl's functional models semantics.The calculus consists of axioms and rulesof inference for reasoning about causalrelevance relationships.We extend the set of known axioms for causalrelevance with three new axioms, andintroduce two new rules of inference forreasoning about specific subclasses ofmodels.These subclasses give a more refinedcharacterization of causal models than the one given in Halpern's axiomatizationof counterfactual reasoning.Finally, we show how the calculus for causalrelevance can be used in the task ofidentifying causal structure from non-observational data.

Citations

• Causal Discovery from Changes
• Scientific Explanation and the Causal Structure of the World

Related

• Causality Without Causal Models
• Axiomatizing Causal Reasoning
• Axiomatizing Causal Reasoning
• Causal Modeling Semantics for Counterfactuals with Disjunctive Antecedents
• Causal programming: inference with structural causal models as finding instances of a relation