The framework starts with the idea that the purpose of causal structure is to understand and predict the effects of intervention. Those fields have ushered in new insights about causal models by thinking about how to represent causal structure mathematically, in a framework that uses graphs and probability theory to develop what are called causal Bayesian networks. In cognitive terms, how do people construct and reason with the causal models we use to represent our world? A revolution is occurring in how statisticians, philosophers, and computer scientists answer this question. To understand how thought serves action requires understanding how people conceive of the relation between cause and effect, between action and outcome. Human beings are active agents who can think. ![]() Genre: Psychology,Books,Health, Mind & Body,.
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