Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
The Shadows and Shallows of Explanation
Minds and Machines
Possibilistic causality consistency problem based on asymmetrically-valued causal model
Fuzzy Sets and Systems - Possibility theory and fuzzy logic
How causal knowledge simplifies decision-making
Minds and Machines
Learning causality and intentional actions
Proceedings of the 2006 international conference on Towards affordance-based robot control
Linearity properties of bayes nets with binary variables
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
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I argue that psychologists interested in human causal judgment should understand and adopt a representation of causal mechanisms by directedgraphs that encode conditional independence (screening off) relations. Iillustrate the benefits of that representation, now widely used in computerscience and increasingly in statistics, by (i) showing that a dispute inpsychology between ’mechanist‘ and ’associationist‘ psychological theoriesof causation rests on a false and confused dichotomy; (ii) showing that arecent, much-cited experiment, purporting to show that human subjects,incorrectly let large causes ’overshadow‘ small causes, misrepresents themost likely, and warranted, causal explanation available to the subjects,in the light of which their responses were normative; (iii) showing how arecent psychological theory (due to P. Cheng) of human judgment of causalpower can be considerably generalized: and (iv) suggesting a range ofpossible experiments comparing human and computer abilities to extractcausal information from associations.