Computational philosophy of science
Computational philosophy of science
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Abductive inference models for diagnostic problem-solving
Abductive inference models for diagnostic problem-solving
Causality and model abstraction
Artificial Intelligence
The Art of Causal Conjecture
Explanation Patterns: Understanding Mechanical and Creatively
Explanation Patterns: Understanding Mechanical and Creatively
The Shadows and Shallows of Explanation
Minds and Machines
It's okay to be wrong: recognizing mechanistic reasoning during student inquiry
ICLS '06 Proceedings of the 7th international conference on Learning sciences
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Why do people get sick? I argue that a disease explanation is best thought of as causal network instantiation, where a causal network describes the interrelations amongmultiple factors, and instantiation consists of observational or hypothetical assignment of factors to the patient whose disease is being explained. This paper first discusses inference fromcorrelation to causation, integrating recent psychological discussions of causal reasoning with epidemiological approaches to understanding disease causation, particularly concerning ulcers and lung cancer. It then shows how causal mechanisms represented by causal networks can contribute to reasoning involving correlation and causation. The understanding of causation and causal mechanisms provides the basis for a presentation of the causal network instantiation model of medical explanation.