Causal mechanism and probability: A normative approach

Causal mechanism and probability: A normative approach (1998)

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by Clark Glymour , Patricia W. Cheng

Venue:	In M. Oaksford & N. Chater (Eds.), Rational models of cognition
Citations:	16 - 1 self

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Datum	Value	Source
TITLE	Causal mechanism and probability: A normative approach	INFERENCE
AUTHOR NAME	Clark Glymour	SVM HeaderParse 0.2
AUTHOR AFFIL	University of California, San Diego University of California, Los Angeles; Carnegie Mellon University	SVM HeaderParse 0.2
AUTHOR NAME	Patricia W. Cheng	SVM HeaderParse 0.2
AUTHOR AFFIL	University of California, San Diego University of California, Los Angeles; Carnegie Mellon University	SVM HeaderParse 0.2
ABSTRACT	The rationality of human causal judgments has been the focus of a great deal of recent research. We argue against two major trends in this research, and for a quite different way of thinking about causal mechanisms and probabilistic data. Our position rejects a false dichotomy between "mechanistic " and "probabilistic " analyses of causal inference-- a dichotomy that both overlooks the nature of the evidence that supports the induction of mechanisms and misses some important probabilistic implications of mechanisms. This dichotomy has obscured an alternative conception of causal learning: for discrete events, a central adaptive task is to induce causal mechanisms in the environment from probabilistic data and prior knowledge. Viewed from this perspective, it is apparent that the probabilistic norms assumed in the human causal judgment literature often do not map onto the mechanisms generating the probabilities. Our alternative conception of causal judgment is more congruent with both scientific uses of the notion of causation and observed causal judgments of untutored reasoners. We illustrate some of the relevant variables under this conception, using a framework for causal representation now widely adopted in computer science and, increasingly, in statistics. We also review the formulation and evidence for a theory of human causal induction (Cheng, 1997) that adopts this alternative conception. 1. The Old Mechanism Approach A long and still popular tradition in the study of human causal reasoning insists on a dramatic bifurcation between "mechanistic " conceptions of causalGlymour & Cheng inference and "probabilistic " or "covariational " conceptions of this process (e.g., Ahn	SVM HeaderParse 0.2
YEAR	1998	INFERENCE
VENUE	In M. Oaksford & N. Chater (Eds.), Rational models of cognition	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	295--313	INFERENCE
CITATIONS	40 found	ParsCit 1.0