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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Page 4
... Markov assumption. The Causal Markov Assumption For any variable X in an acyclic causal graph, X is independent of all other variables in the graph (except for its own direct and indirect effects) conditional on its own direct causes ...
... Markov assumption. The Causal Markov Assumption For any variable X in an acyclic causal graph, X is independent of all other variables in the graph (except for its own direct and indirect effects) conditional on its own direct causes ...
Page 5
... Markov assumption generalizes this screening-off principle to all acyclic causal graphs. Thus, if we know the structure of the graph and know the values of some of the variables in the graph, we can make consistent predictions about the ...
... Markov assumption generalizes this screening-off principle to all acyclic causal graphs. Thus, if we know the structure of the graph and know the values of some of the variables in the graph, we can make consistent predictions about the ...
Page 7
... 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 (Glymour & Cooper, 1999; Spirtes et al., 1993) ...
... 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 (Glymour & Cooper, 1999; Spirtes et al., 1993) ...
Page 23
... Markov condition CM, according to which, conditional on its direct causes, every variable is independent of every other variable, singly or in combination, except for its effects. Given this assumption, both structures 1-1 and 1-2 imply ...
... Markov condition CM, according to which, conditional on its direct causes, every variable is independent of every other variable, singly or in combination, except for its effects. Given this assumption, both structures 1-1 and 1-2 imply ...
Page 66
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Causal Learning: Psychology, Philosophy, and Computation Alison Gopnik,Laura Schulz Limited preview - 2007 |
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