The authors outline a cognitive and computational account of causal learning in children. They propose that children use specialized cognitive systems that allow them to recover an accurate “causal map ” of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or Bayes nets. Children’s causal learning and inference may involve computations similar to those for learning causal Bayes nets and for predicting with them. Experimental results suggest that 2to 4-year-old children construct new causal maps and that their learning is consistent with the Bayes net formalism.

We show that if any number of variables are allowed to be simultaneously and independently randomized in any one experiment, log 2 (N) + 1 experiments are su#cient and in the worst case necessary to determine the causal relations among N 2 variables when no latent variables, no sample selection bias and no feedback cycles are present. For all K, 0 2 N we provide an upper bound on the number experiments required to determine causal structure when each experiment simultaneously randomizes K variables.

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