An Information-Theoretic Causal Power Theory

2005 ◽  
pp. 805-811 ◽  
Author(s):  
Lucas R. Hope ◽  
Kevin B. Korb
Keyword(s):  
Causal Power ◽  
Power Theory ◽  
Information Theoretic

A new causal power theory

2011 ◽  
pp. 628-652 ◽  
Author(s):  
Kevin B. Korb ◽  
Erik P. Nyberg ◽  
Lucas Hope
Keyword(s):  
Causal Power ◽  
Power Theory

Judging the Importance of Constant and Variable Candidate Causes: A Test of the Power PC Theory

1998 ◽  
Vol 51 (1) ◽  
pp. 65-84 ◽  
Author(s):  
Frédéric Vallée-Tourangeau ◽  
Robin A. Murphy ◽  
Susan Drew ◽  
A.G. Baker
Keyword(s):  
Associative Learning ◽  
Causal Power ◽  
Base Rate ◽  
The Other ◽  
Associative Model ◽  
Contrast Model ◽  
Causal Induction ◽  
Power Theory ◽  
Factorial Combination ◽  
Target Effect

In two causal induction experiments subjects rated the importance of pairs of candidate causes in the production of a target effect; one candidate was present on every trial (constant cause), whereas the other was present on only some trials (variable cause). The design of both experiments consisted of a factorial combination of two values of the variable cause's covariation with the effect and three levels of the base rate of the effect. Judgements of the constant cause were inversely proportional to the level of covariation of the variable cause but were proportional to the base rate of the effect. The judgements were consistent with the predictions derived from the Rescorla-Wagner (1972) model of associative learning and with the predictions of the causal power theory of the probabilistic contrast model (Cheng, 1997) or “power PC theory”. However, judgements of the importance of the variable candidate cause were proportional to the base rate of the effect, a phenomenon that is in some cases anticipated by the power PC theory. An alternative associative model, Pearce's (1987) similarity-based generalization model, predicts the influence of the base rate of the effect on the estimates of both the constant and the variable cause.

From covariation to causation: A causal power theory.

1997 ◽  
Vol 104 (2) ◽  
pp. 367-405 ◽  
Author(s):  
Patricia W. Cheng
Keyword(s):  
Causal Power ◽  
Power Theory

Information-Theoretic Causal Power

2008 ◽  
pp. 231-265 ◽  
Author(s):  
Kevin B. Korb ◽  
Lucas R. Hope ◽  
Erik P. Nyberg
Keyword(s):  
Causal Power ◽  
Information Theoretic

A Causal-Power Theory of Focal Sets

1996 ◽  
pp. 313-355 ◽  
Author(s):  
Patricia W. Cheng ◽  
Jooyong Park ◽  
Aaron S. Yarlas ◽  
Keith J. Holyoak
Keyword(s):  
Causal Power ◽  
Power Theory

Beyond metaphors and semantics: A framework for causal inference in neuroscience

2019 ◽  
Vol 42 ◽  
Author(s):  
Roberto A. Gulli
Keyword(s):  
Causal Inference ◽  
Causal Power

Abstract The long-enduring coding metaphor is deemed problematic because it imbues correlational evidence with causal power. In neuroscience, most research is correlational or conditionally correlational; this research, in aggregate, informs causal inference. Rather than prescribing semantics used in correlational studies, it would be useful for neuroscientists to focus on a constructive syntax to guide principled causal inference.

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