Cause, Responsibility, and Blame: oA Structural-Model Approach

TL;DR

This paper reviews and refines structural-model definitions of causality, responsibility, and blame, incorporating normality and epistemic factors, and compares these to Wright's NESS test.

Contribution

It introduces a refined causality framework that accounts for normality, responsibility, and blame, extending existing models with new conceptual insights.

Findings

Refined causality definition includes normality and typicality.

Degree of responsibility varies with outcomes, e.g., election results.

Degree of blame incorporates agents' epistemic states.

Abstract

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.