David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
& Carnegie Mellon University 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.
|Keywords||No keywords specified (fix it)|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library||
References found in this work BETA
No references found.
Citations of this work BETA
No citations found.
Similar books and articles
Daniel J. Nicholson (2012). The Concept of Mechanism in Biology. Studies in History and Philosophy of Science Part C 43 (1):152-163.
Frederick S. Ellett Jr & David P. Ericson (1983). The Logic of Causal Methods in Social Science. Synthese 57 (1):67 - 82.
George L. Newsome (2003). The Debate Between Current Versions of Covariation and Mechanism Approaches to Causal Inference. Philosophical Psychology 16 (1):87 – 107.
Christopher Read Hitchcock (1993). A Generalized Probabilistic Theory of Causal Relevance. Synthese 97 (3):335 - 364.
Alex Broadbent (2011). Inferring Causation in Epidemiology: Mechanisms, Black Boxes, and Contrasts. In Phyllis McKay Illari, Federica Russo & Jon Williamson (eds.), Causality in the Sciences. Oxford University Press. 45--69.
Thomas L. Griffiths, David M. Sobel, Joshua B. Tenenbaum & Alison Gopnik (2011). Bayes and Blickets: Effects of Knowledge on Causal Induction in Children and Adults. Cognitive Science 35 (8):1407-1455.
Clark Glymour (1998). Learning Causes: Psychological Explanations of Causal Explanation. [REVIEW] Minds and Machines 8 (1):39-60.
Added to index2009-01-28
Total downloads39 ( #42,817 of 1,098,638 )
Recent downloads (6 months)1 ( #285,836 of 1,098,638 )
How can I increase my downloads?