## Causal Learning: Psychology, Philosophy, and ComputationAlison Gopnik, Laura Schulz Understanding causal structure is a central task of human cognition. Causal learning underpins the development of our concepts and categories, our intuitive theories, and our capacities for planning, imagination and inference. During the last few years, there has been an interdisciplinary revolution in our understanding of learning and reasoning: Researchers in philosophy, psychology, and computation have discovered new mechanisms for learning the causal structure of the world. This new work provides a rigorous, formal basis for theory theories of concepts and cognitive development, and moreover, the causal learning mechanisms it has uncovered go dramatically beyond the traditional mechanisms of both nativist theories, such as modularity theories, and empiricist ones, such as association or connectionism. |

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... , RI 02912 Jessica Sommerville Department of Psychology and Institute for Learning & Brain Sciences University of Washington Seattle , WA 98195 structure of a causal graph constrains the

... , RI 02912 Jessica Sommerville Department of Psychology and Institute for Learning & Brain Sciences University of Washington Seattle , WA 98195 structure of a causal graph constrains the

**conditional probabilities**among X CONTRIBUTORS. Page 3

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**conditional probabilities**and interventions, and perhaps most significantly discriminate between**conditional probabilities**and interventions and counterfactuals. So, I decided to move to Carnegie Tech for graduate school and work on ... Page 4

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**conditional probabilities**among the variables in that graph , no mat- ter what the variables are or what the parameterization of the graph is . In particular , it constrains the conditional independencies among those variables . Given a ... Page 5

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**conditional probability**among the three variables . Suppose you keep track of all the times you drink and party and ... probabilities ( Pearl , 1988 ) . Many real - life inferences involve complex combinations of condi- tional probabilities ... Page 6

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**conditional**dependencies among the variables after the intervention can be read from this altered graph. Suppose ...**probabilities**to inferences about interventions and vice versa. These two assumptions, then, allow us to take a par- ticular ...### Other editions - View all

Causal Learning: Psychology, Philosophy, and Computation Alison Gopnik,Laura Schulz Limited preview - 2007 |

Causal Learning: Psychology, Philosophy, and Computation Alison Gopnik,Laura Schulz Limited preview - 2007 |

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actions adults algorithms Bayesian inference Bayesian networks behavior beliefs birth control pills blicket detector Cambridge causal Bayes nets causal inference causal knowledge causal learning causal Markov condition causal model causal networks causal power causal reasoning causal relations causal relationships causal strength causal structure causal system chapter Cognitive Science common cause computational condition conditional independence conditional probabilities correlation counterfactuals covariation cues deterministic Development Developmental Developmental Psychology domain effect evidence example experiments explanations Figure framework Fuel Intake Glymour Gopnik graph schema graphical models Griffiths Hagmayer human independent infants intervention interventionist intuitive theories Journal of Experimental Lagnado Laplace learners manipulated Markov Markov random field mechanism Meltzoff object observed outcome participants Piston predictions prior probabilistic probabilistic graphical models probability distribution psychological question Reichenbach represent representation Schulz Sloman Sobel specific statistical stickball Tenenbaum thrombosis tion trials underlying understanding unobserved cause variables Waldmann Wellman Woodward