Intervention, correlation and causal learning:
		 Why children really ARE  scientists.
			    Alison Gopnik

Abstract: Children, like scientists, face the difficult problem of
inferring the causal structure of the world from patterns of evidence.
Scientists classically learn about causal structure by looking at
patterns of correlation (in observational studies) or at the outcomes
of interventions ( in experiments) or, most commonly, at combinations
of interventions and correlations.  Recent advances in statistics,
computer science, and philosophy provide a normative account of such
inferences. The causal Bayes net formalism provides a unified
mathematical account of causation, correlation and intervention, and
shows that given a few general assumptions, a wide range of accurate
causal inferences can be made. We propose that even very young
children use similar assumptions and inferences implicitly in their
everyday causal learning. In several experiments we show that
preschoolers can draw accurate causal inferences from patterns of
interventions and correlations even when there are no spatio-temporal,
mechanistic, or associative cues to causal relations.

Thursday, October 2nd, 12:15 - 1:30
Cordura Hall, Room 100 (at CSLI)
with burritos at 12 for $3