Statistical Inference and Causal Reasoning
ROCHESTER UNIV NY DEPT OF COMPUTER SCIENCE
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In this paper, we show how degrees of belief about causal predictions can be derived from statistics about the truth of properties over time. By using statistical information analogous to traditional non-statistical rules of causation and knowledge about a specific time point, predictions can be made about both the change and persistence of properties for the next time point with some degree of belief. We show how to incrementally compute this degree of belief by combining statistics conditioned on successively larger subsets of the reasoners knowledge. Furthermore, we solve the qualification problem through a powerful heuristic that builds these subsets by considering properties with highest impact first. This heuristic ignores relatively unlikely, redundant, or unrelated properties when deriving a prediction, while directing the focus along causal chains. The iterative formula and this heuristic define an algorithm that produces predictions with quickly increasing confidence, allowing computational resources to trade off against-accuracy.
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