## Causal Learning: Psychology, Philosophy, and ComputationUnderstanding 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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Causal graphical models capture just the right kind of asymmetries in causal relations, allow one to generate the appropriate predictions about

Causal graphical models capture just the right kind of asymmetries in causal relations, allow one to generate the appropriate predictions about

**conditional probabilities**and interventions, and perhaps most significantly discriminate ... Page 4

Scientists infer causal structure by observing the patterns of

Scientists infer causal structure by observing the patterns of

**conditional probability**among events (as in ... Causal Structure and**Conditional Probabilities**The Bayes net formalism makes systematic connections between the causal ... Page 5

You can discriminate between these two graphs by looking at the patterns of

You can discriminate between these two graphs by looking at the patterns of

**conditional probability**among the three variables. Suppose you keep track of all the times you drink and party and examine the effects on your insomnia. Page 6

The conditional dependencies among the variables after the intervention can be read from this altered graph. ... then, allow us to take a particular causal structure and accurately predict the

The conditional dependencies among the variables after the intervention can be read from this altered graph. ... then, allow us to take a particular causal structure and accurately predict the

**conditional probabilities**of events, ... Page 7

Given the faithfulness assumption, it is possible to infer complex causal structure from patterns of

Given the faithfulness assumption, it is possible to infer complex causal structure from patterns of

**conditional probability**and intervention (Glymour & Cooper, 1999; Spirtes et al., 1993). Computationally tractable learning algorithms ...### What people are saying - Write a review

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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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