## 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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**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 probability**among events (as in statistical analysis), by examining the consequences of interventions ... Probabilities The Bayes net formalism makes systematic connections between the causal hypotheses that are represented ... 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 and discovered that representing the variables in a directed graph allowed them to do this. The graph ... Page 6

... probabilities to inferences about interventions and vice versa. These two assumptions, then, allow us to take a particular causal structure and accurately predict the

... probabilities to inferences about interventions and vice versa. These two assumptions, then, allow us to take a particular causal structure and accurately predict the

**conditional probabilities**of events, and the consequences of ... Page 7

... conditional independencies are consequences of the Markov assumption applied to the graph. Given the faithfulness assumption, it is possible to infer complex causal structure from patterns of

... conditional independencies are consequences of the Markov assumption applied to the graph. Given the faithfulness assumption, it is possible to infer complex causal structure from patterns of

**conditional probability**and intervention ...### 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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