The Role of Mechanism Beliefs in Causal Reasoning

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Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI (2019, background): “Relevant 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,…”
The misunderstood limits of folk science: an illusion of explanatory depth (2002, background): “Folk theories, we claim, are even more fragmentary and skeletal, but laypeople, unlike some scientists, usually remain unaware of the incompleteness of their theories (Ahn & Kalish, 2000; Dunbar, 1995; diSessa, 1983).”
Recognizing mechanistic reasoning in student scientific inquiry: A framework for discourse analysis developed from philosophy of science (2008, background): “Other research presents evidence that knowledge of mechanisms is in fact what helps us identify and understand the causal structure of the world (e.g., Ahn & Kalish, 2000; Ahn, Kalish, Medin, & Gelman, 1995; Shultz, 1982).”
Judgment dissociation theory: an analysis of differences in causal, counterfactual, and covariational reasoning. (2003, background): “Hence, JDT draws conceptual connections to accounts that posit the importance of perceived mechanisms (Ahn & Kalish, 2000 Player 2 directly influenced the actual outcome.”