Causes and explanations revisited

• Authors: James D. Park
• Venue: IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
• Year: 2003

This paper reconsiders the notions of actual cause and explanation in functional causal models. We demonstrate that isomorphic causal models can generate intuitively different causal pronouncements. This occurs because psychological factors not represented in the model determine what criteria we use to determine causation. This partially explains the difficulty encountered in previous attempts to define actual cause. Freed from trying fit all examples to match intuition directly (which is not possible using only the information in causal models), we provide definitions for causation matching the different causal criteria we intuitively apply. This formulation avoids difficulties associated with previous definitions, and allows a more refined discussion of what constitutes a cause in a given situation. The definitions of actual cause also allow for more refined formulations of explanation.