Learning causes: Psychological explanations of causal explanation

Learning causes: Psychological explanations of causal explanation (1998)

by Clark Glymour

Venue:	Minds and Machines
Citations:	22 - 0 self

Datum	Value	Source
TITLE	Learning causes: Psychological explanations of causal explanation	INFERENCE
AUTHOR NAME	Clark Glymour	SVM HeaderParse 0.2
AUTHOR AFFIL	University of California at San Diego and Carnegie Mellon University	SVM HeaderParse 0.2
ABSTRACT	Abstract. I argue that psychologists interested in human causal judgment should understand and adopt a representation of causal mechanisms by directed graphs that encode conditional independence (screening off) relations. I illustrate the benefits of that representation, now widely used in computer science and increasingly in statistics, by (i) showing that a dispute in psychology between ‘mechanist’ and ‘associationist ’ psychological theories of causation rests on a false and confused dichotomy; (ii) showing that a recent, much-cited experiment, purporting to show that human subjects, incorrectly let large causes ‘overshadow ’ small causes, misrepresents the most likely, and warranted, causal explanation available to the subjects, in the light of which their responses were normative; (iii) showing how a recent psychological theory (due to P. Cheng) of human judgment of causal power can be considerably generalized: and (iv) suggesting a range of possible experiments comparing human and computer abilities to extract causal information from associations.	SVM HeaderParse 0.2
YEAR	1998	INFERENCE
VENUE	Minds and Machines	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
PAGES	39--60	INFERENCE
VOLUME	8	INFERENCE
CITATIONS	24 found	ParsCit 1.0