Literature DB >> 15065918

Assessing interactive causal influence.

Laura R Novick1, Patricia W Cheng.   

Abstract

The discovery of conjunctive causes--factors that act in concert to produce or prevent an effect--has been explained by purely covariational theories. Such theories assume that concomitant variations in observable events directly license causal inferences, without postulating the existence of unobservable causal relations. This article discusses problems with these theories, proposes a causal-power theory that overcomes the problems, and reports empirical evidence favoring the new theory. Unlike earlier models, the new theory derives (a). the conditions under which covariation implies conjunctive causation and (b). functions relating observable events to unobservable conjunctive causal strength. This psychological theory, which concerns simple cases involving 2 binary candidate causes and a binary effect, raises questions about normative statistics for testing causal hypotheses regarding categorical data resulting from discrete variables.

Mesh:

Year:  2004        PMID: 15065918     DOI: 10.1037/0033-295X.111.2.455

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


  17 in total

Review 1.  A cognitive neuroscience framework for understanding causal reasoning and the law.

Authors:  Jonathan A Fugelsang; Kevin N Dunbar
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2004-11-29       Impact factor: 6.237

2.  From counterfactuals to sufficient component causes and vice versa.

Authors:  Tyler J VanderWeele; Miguel A Hernán
Journal:  Eur J Epidemiol       Date:  2006       Impact factor: 8.082

3.  BUCKLE: a model of unobserved cause learning.

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

4.  Accounting for occurrences: an explanation for some novel tendencies in causal judgment from contingency information.

Authors:  Peter A White
Journal:  Mem Cognit       Date:  2009-06

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

Authors:  Christian C Luhmann; Woo-Kyoung Ahn
Journal:  Psychol Rev       Date:  2005-07       Impact factor: 8.934

6.  The optimal level of fuzz: Case studies in a methodology for psychological research.

Authors:  Arthur B Markman; Jennifer S Beer; Lisa R Grimm; Jonathan R Rein; W Todd Maddox
Journal:  J Exp Theor Artif Intell       Date:  2009-09-01       Impact factor: 2.340

7.  Invited Commentary: The Continuing Need for the Sufficient Cause Model Today.

Authors:  Tyler J VanderWeele
Journal:  Am J Epidemiol       Date:  2017-06-01       Impact factor: 4.897

Review 8.  Reasoning about causal relationships: Inferences on causal networks.

Authors:  Benjamin Margolin Rottman; Reid Hastie
Journal:  Psychol Bull       Date:  2013-04-01       Impact factor: 17.737

9.  Effect of grouping of evidence types on learning about interactions between observed and unobserved causes.

Authors:  Benjamin Margolin Rottman; Woo-kyoung Ahn
Journal:  J Exp Psychol Learn Mem Cogn       Date:  2011-08-08       Impact factor: 3.051

10.  Explaining compound generalization in associative and causal learning through rational principles of dimensional generalization.

Authors:  Fabian A Soto; Samuel J Gershman; Yael Niv
Journal:  Psychol Rev       Date:  2014-07       Impact factor: 8.934

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