From covariation to causation: A causal power theory.

By Patricia W. Cheng. 1997

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Citations in the corpus

Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI (2019, background): “…research on causal reasoning relevant to explanation includes: Ahn (1998); Ahn et al. (1995); Ahn and Kalish (2000); Chater and Oaksford, (2006); Cheng (1997, 2000); Einhorn and Hogarth (1986); Eiter and Lukasiewicz (2002, 2006); Feltovich, Hoffman, Woods, and Roesler (2004); Graesser, Baggett,…”
A theory of causal learning in children: causal maps and Bayes nets. (2004, methodology): “Cheng’s (1997) learning method does not use a statistical procedure or a learning rate but estimates causal powers directly from the observed frequencies.”
Reasoning About Multiple Variables: Control of Variables Is Not the Only Challenge (2007, unknown): “Science Education DOI 10.1002/sce studies of multivariable causal inference (Cheng, 1997, Downing, Sternberg, & Ross, 1985; Glymour, 2001; Hilton & Slugoski, 1986; Schustack & Sternberg, 1981; Sloman & Lagnado, 2005; Spellman, 1996; Steyvers, Tenenbaum, Wagenmakers, & Blum, 2003; Waldmann &…”
A functional-cognitive framework for attitude research (2013, methodology): “Although philosophers and psychologists debated for centuries about how causality can be inferred (e.g., Cheng, 1997; Hume, 1739/1987; Kant, 1781/ 1965; Sosa & Tooley, 1993), there is general agreement that the experimental method provides a useful tool to establish the presence of a causal…”
Connecting Scientific Reasoning and Causal Inference (2004, background): “…features an individual deems theoretically relevant, the findings of Cheng and her colleagues show consistent support for her causal power theory, that is, the variable whose presence versus absence consistently yields the largest contrast receives the strongest endorsement as cause (Cheng, 1997).”
A Sampling Approach to Biases in Conditional Probability Judgments: Beyond Baserate-Neglect and Statistical Format (2000, background): “These variables often belong to natural kinds (Rothbart & Taylor, 1992), have causal power (Cheng, 1997; White, 1995), or may be wholistic rather than specific (Gavanski & Hui, 1992); or the choice of a sampling space is influenced by motivated attention (Gigerenzer & Hug, 1992; McMulIen et al.”
Judgments of cause and blame: the effects of intentionality and foreseeability. (2008, background): “Causal judgments about general laws (e.g., smoking causes cancer) are often analyzed in terms of probabilistic models (Cheng, 1997; Shanks, 2004), although the domain is usually restricted to relations between physical events.”
Knowledge mediates the timeframe of covariation assessment in human causal induction (2002, methodology): “One of the key advantages of covariationbased theories (including associative learning, but also the computational causal power approach, Cheng, 1997) is that they explicitly account for knowledge gained from probabilistic feedback. Finally, the dependent measures in Schlottmann’s study were based o”
Causal Learning (2012, methodology): “How, then, does the causal power theory (Cheng, 1997) go beyond the proximal stimulus and explain the various ways in which covariation does not imply causation? Th e path through the derivation of the estimation of causal strength reveals the answers.”
Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense (2020, background): “[132] Patricia W Cheng, From covariation to causation: a causal power theory, Psychological Review 104 (2) (1997) 367–405.”
Misconceived Causal Explanations for Emergent Processes (2012, background): “However, even though simulations can make the second-order interaction features visible, it is still important to point them out either explicitly or to scaffold students to see them because they may be difficult to notice on one’s own.”