Learning From Doing: Intervention and Causal Inference
This chapter starts from the premise that much of children's knowledge takes the form of abstract, coherent, causal claims that are learned from, and defeasible by, evidence. This view is consistent with an interventionist view of causal knowledge, formalized in computational models using causal Bayes net representations. The chapter reviews empirical studies suggesting that, consistent with this account, preschoolers use patterns of evidence to: a) create novel, effective interventions; b) infer the structure of causal relationships, including relationships involving unobserved causes; c) accurately predict distinct outcomes from observed evidence and evidence generated by interventions; d) integrate novel evidence with prior beliefs; and e) distinguish informative interventions from confounded ones.
Duke Scholars
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