Familiar Problems in Probabilistic Causal Reasoning
ROCHESTER UNIV NY DEPT OF COMPUTER SCIENCE
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In most work on causal reasoning, an agents 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 Kanazawas 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 Kanazawas solutions from a more traditional non-probabilistic causal framework.
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- Statistics and Probability