## 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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Contents PART I: Contributors ix Introduction 1 Alison Gopnik and Laura Schulz CAUSATION AND

Contents PART I: Contributors ix Introduction 1 Alison Gopnik and Laura Schulz CAUSATION AND

**INTERVENTION**1 Interventionist Theories of Causation in Psychological Perspective 19 Jim Woodward 2 Infants' Causal Learning:**Intervention**, ... Page 5

Bayes Nets and

Bayes Nets and

**Interventions**Why think of these graphs as representations of causal relations among variables, ... The Bayes net formalism captures these relations between causation,**intervention**, and conditional probability through a ... Page 6

The

The

**Intervention**Assumption A variable I is an**intervention**on a variable X in a causal graph if and only if (a) I is ... we can accurately predict the effects of**interventions**on particular variables in a graph on other variables. Page 7

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

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 ... Page 8

Those representations allow children to make predictions, perform

Those representations allow children to make predictions, perform

**interventions**, and even generate counterfactuals. ... Plus, even the very smallest sprogs can combine information from observation and**intervention**.### 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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