David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
Philosophical Psychology 16 (1):87 – 107 (2003)
Current psychological research on causal inference is dominated by two basic approaches: the covariation approach and the mechanism approach. This article reviews these two approaches, evaluates the contributions and limitations of each approach, and suggests how these approaches might be integrated into a more comprehensive framework. Covariation theorists assume that cognizers infer causal relations from conditional probabilities computed over samples of multiple events, but they do not provide an adequate account of how cognizers constrain their search for candidate causes and relevant evidence. Mechanism theorists assume that cognizers use their knowledge of potential mechanisms to infer the causes of individual events, but they do not account for the origins of this kind of knowledge. Theorists might integrate these approaches into a framework that overcomes these limitations by (1) examining important relations between cognizers' beliefs about the nature of causality, the logic of causal inference, and the processes cognizers use to make causal inferences, and (2) providing a more complete account of cognizers' conceptions of causality and the origins of those conceptions.
|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
David Rose & David Danks (2013). In Defense of a Broad Conception of Experimental Philosophy. Metaphilosophy 44 (4):512-532.
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.
Similar books and articles
Frederick S. Ellett Jr & David P. Ericson (1983). The Logic of Causal Methods in Social Science. Synthese 57 (1):67 - 82.
Steven Rappaport (1996). Inference to the Best Explanation: Is It Really Different From Mill's Methods? Philosophy of Science 63 (1):65-80.
Judea Pearl (2000). Causality: Models, Reasoning, and Inference. Cambridge University Press.
Stathis Psillos (2004). A Glimpse of the Secret Connexion: Harmonizing Mechanisms with Counterfactuals. Perspectives on Science 12 (3):288-319.
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.
Jonathan A. Fugelsang, Valerie A. Thompson & Kevin N. Dunbar (2006). Examining the Representation of Causal Knowledge. Thinking and Reasoning 12 (1):1 – 30.
Daniel Steel (2010). Cartwright on Causality: Methods, Metaphysics and Modularity. Economics and Philosophy 26 (1):77-86.
Alison Gopnik (2004). Children's Causal Inferences From Indirect Evidence: Backwards Blocking and Bayesian Reasoning in Preschoolers. Cognitive Science 28 (3):303-333.
David Danks (2005). The Supposed Competition Between Theories of Human Causal Inference. Philosophical Psychology 18 (2):259 – 272.
Added to index2009-01-28
Total downloads5 ( #234,761 of 1,099,953 )
Recent downloads (6 months)1 ( #304,017 of 1,099,953 )
How can I increase my downloads?