Graded Causation and Defaults

TL;DR

This paper introduces a formal framework for actual causation that incorporates defaults, typicality, and normality, making causation judgments graded and context-sensitive, and demonstrates its application to standard cases.

Contribution

It develops a novel formal account of causation that integrates normality considerations, addressing limitations of previous binary causation models.

Findings

The framework models causation as graded and comparative.

It successfully handles standard causation cases.

It incorporates defaults and normality into causation reasoning.

Abstract

Recent work in psychology and experimental philosophy has shown that judgments of actual causation are often influenced by consideration of defaults, typicality, and normality. A number of philosophers and computer scientists have also suggested that an appeal to such factors can help deal with problems facing existing accounts of actual causation. This paper develops a flexible formal framework for incorporating defaults, typicality, and normality into an account of actual causation. The resulting account takes actual causation to be both graded and comparative. We then show how our account would handle a number of standard cases.