2014/12/09 by Joseph Y. Halpern, Halpern, Joseph Y. · 1 citation
Arts and Humanities · Computer Science · Mathematics · #Advanced Causal Inference Techniques #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Philosophy and History of Science
paper · pdf · doi:10.48550/arxiv.1412.2985
openalex publication_date 2014/12/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
A definition of causality introduced by Halpern and Pearl, which uses structural equations, is reviewed. A more refined definition is then considered, which takes into account issues of normality and typicality, which are well known to affect causal ascriptions. Causality is typically an all-or-nothing notion: either A is a cause of B or it is not. An extension of the definition of causality to capture notions of degree of responsibility and degree of blame, due to Chockler and Halpern, is reviewed. For example, if someone wins an election 11-0, then each person who votes for him is less responsible for the victory than if he had won 6-5. Degree of blame takes into account an agent's epistemic state. Roughly speaking, the degree of blame of A for B is the expected degree of responsibility of A for B, taken over the epistemic state of an agent. Finally, the structural-equations definition of causality is compared to Wright's NESS test.