Structure and Strength in Causal Induction

Autor/inn/en	Griffiths, Thomas L.; Tenenbaum, Joshua B.
Titel	Structure and Strength in Causal Induction
Quelle	In: Cognitive Psychology, 51 (2005) 4, S.334-384 (51 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0010-0285
DOI	10.1016/j.cogpsych.2005.05.004
Schlagwörter	Probability; Logical Thinking; Inferences; Causal Models; Cognitive Psychology; Bayesian Statistics + Suchen Sie Ihr Suchwort? Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Inference; Inferenz; Kausalanalyse; Kognitive Psychologie
Abstract	We present a framework for the rational analysis of elemental causal induction--learning about the existence of a relationship between a single cause and effect--based upon causal graphical models. This framework makes precise the distinction between causal structure and causal strength: the difference between asking whether a causal relationship exists and asking how strong that causal relationship might be. We show that two leading rational models of elemental causal induction, [delta]"P" and causal power, both estimate causal strength, and we introduce a new rational model, causal support, that assesses causal structure. Causal support predicts several key phenomena of causal induction that cannot be accounted for by other rational models, which we explore through a series of experiments. These phenomena include the complex interaction between [delta]"P" and the base-rate probability of the effect in the absence of the cause, sample size effects, inferences from incomplete contingency tables, and causal learning from rates. Causal support also provides a better account of a number of existing datasets than either [delta]"P" or causal power. (Author).
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Update	2017/4/10