Literature DB >> 16060763

The meaning and computation of causal power: comment on Cheng (1997) and Novick and Cheng (2004).

Christian C Luhmann1, Woo-Kyoung Ahn.   

Abstract

D. Hume (1739/1987) argued that causality is not observable. P. W. Cheng (1997) claimed to present "a theoretical solution to the problem of causal induction first posed by Hume more than two and a half centuries ago" (p. 398) in the form of the power PC theory (L. R. Novick & P. W. Cheng, 2004). This theory claims that people's goal in causal induction is to estimate causal powers from observable covariation and outlines how this can be done in specific conditions. The authors first demonstrate that if the necessary assumptions were ever met, causal powers would be self-evident to a reasoner--they are either 0 or 1--making the theory unnecessary. The authors further argue that the assumptions the power PC theory requires to compute causal power are unobtainable in the real world and, furthermore, people are aware that requisite assumptions are violated. Therefore, the authors argue that people do not attempt to compute causal power.

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Year:  2005        PMID: 16060763      PMCID: PMC2677809          DOI: 10.1037/0033-295X.112.3.685

Source DB:  PubMed          Journal:  Psychol Rev        ISSN: 0033-295X            Impact factor:   8.934


  8 in total

1.  Is causal induction based on causal power? Critique of Cheng (1997).

Authors:  K Lober; D R Shanks
Journal:  Psychol Rev       Date:  2000-01       Impact factor: 8.934

2.  Estimating causal strength: the role of structural knowledge and processing effort.

Authors:  M R Waldmann; Y Hagmayer
Journal:  Cognition       Date:  2001-11

3.  From covariation to causation: a test of the assumption of causal power.

Authors:  Marc J Buehner; Patricia W Cheng; Deborah Clifford
Journal:  J Exp Psychol Learn Mem Cogn       Date:  2003-11       Impact factor: 3.051

4.  Controlling for causally relevant third variables.

Authors:  Adam S Goodie; Cristina C Williams; C L Crooks
Journal:  J Gen Psychol       Date:  2003-10

5.  Distinguishing genuine from spurious causes: a coherence hypothesis.

Authors:  Y Lien; P W Cheng
Journal:  Cogn Psychol       Date:  2000-03       Impact factor: 3.468

6.  Assessing interactive causal influence.

Authors:  Laura R Novick; Patricia W Cheng
Journal:  Psychol Rev       Date:  2004-04       Impact factor: 8.934

7.  A probabilistic contrast model of causal induction.

Authors:  P W Cheng; L R Novick
Journal:  J Pers Soc Psychol       Date:  1990-04

Review 8.  A theory of causal learning in children: causal maps and Bayes nets.

Authors:  Alison Gopnik; Clark Glymour; David M Sobel; Laura E Schulz; Tamar Kushnir; David Danks
Journal:  Psychol Rev       Date:  2004-01       Impact factor: 8.934

  8 in total
  3 in total

1.  BUCKLE: a model of unobserved cause learning.

Authors:  Christian C Luhmann; Woo-Kyoung Ahn
Journal:  Psychol Rev       Date:  2007-07       Impact factor: 8.934

2.  The influence of the number of relevant causes on the processing of covariation information in causal reasoning.

Authors:  Kyungil Kim; Arthur B Markman; Tae Hoon Kim
Journal:  Cogn Process       Date:  2016-06-17

3.  Causal learning about tolerance and sensitization.

Authors:  Benjamin Margolin Rottman; Woo-Kyoung Ahn
Journal:  Psychon Bull Rev       Date:  2009-12
  3 in total

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