Familiar Problems in Probabilistic Causal Reasoning
Abstract
In most work on causal reasoning, an agent's knowledge assigns one of three values to domain facts: yes, no, or maybe. These values are not sufficient, however, to represent the statistical information available in many interesting domains (arguably including most realistic domains). Thus some recent approaches to causal reasoning have concentrated on representation and inference with probabilistic degrees of belief (DK88, Han88, SH88). We have found that proabilistic approaches to causality suffer from some of the same hard problems as traditional approaches. In particular the frame and qualification problems arise in subtle ways, and it is important to realize when such profound representational problems exit. The problems implicitly motivate the representational primitives of Dean and Kanazawa's approach, but we find fault with their choice of primitives. In this paper, we first describe the persistence and qualification problems in a probabilistic setting, then explain and criticize Dean and Kanazawa's solutions from a more traditional non-probabilistic causal framework.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 1989
- Accession Number
- ADA250604
Entities
People
- Jay Weber
Organizations
- University of Rochester