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  1. A Theory of Causal Learning in Children: Causal Maps and Bayes Nets.Alison Gopnik, Clark Glymour, Laura Schulz, Tamar Kushnir & David Danks - 2004 - Psychological Review 111 (1):3-32.
    We propose that children employ specialized cognitive systems that allow them to recover an accurate “causal map” of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or “Bayes nets”. Children’s causal learning and inference may involve computations similar to those for learning causal Bayes nets and for predicting with them. Experimental results suggest that 2- to 4-year-old children (...)
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  • Making sense of causal relations. A cross-cultural and cross-linguistic study.Olivier Le Guen, Jana Samland, Thomas Friedrich, Daniel Hanus & Penelope Brown - 2015 - Frontiers in Psychology 6.
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  • The Oxford Handbook of Causal Reasoning.Michael Waldmann (ed.) - 2017 - Oxford, England: Oxford University Press.
    Causal reasoning is one of our most central cognitive competencies, enabling us to adapt to our world. Causal knowledge allows us to predict future events, or diagnose the causes of observed facts. We plan actions and solve problems using knowledge about cause-effect relations. Without our ability to discover and empirically test causal theories, we would not have made progress in various empirical sciences. In the past decades, the important role of causal knowledge has been discovered in many areas of cognitive (...)
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  • Bayes and Blickets: Effects of Knowledge on Causal Induction in Children and Adults.Thomas L. Griffiths, David M. Sobel, Joshua B. Tenenbaum & Alison Gopnik - 2011 - Cognitive Science 35 (8):1407-1455.
    People are adept at inferring novel causal relations, even from only a few observations. Prior knowledge about the probability of encountering causal relations of various types and the nature of the mechanisms relating causes and effects plays a crucial role in these inferences. We test a formal account of how this knowledge can be used and acquired, based on analyzing causal induction as Bayesian inference. Five studies explored the predictions of this account with adults and 4-year-olds, using tasks in which (...)
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  • The effect of effects on effectiveness: A boon-bane asymmetry.Abigail B. Sussman & Daniel M. Oppenheimer - 2020 - Cognition 199 (C):104240.
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  • Foundations of a Probabilistic Theory of Causal Strength.Jan Sprenger - 2018 - Philosophical Review 127 (3):371-398.
    This paper develops axiomatic foundations for a probabilistic-interventionist theory of causal strength. Transferring methods from Bayesian confirmation theory, I proceed in three steps: I develop a framework for defining and comparing measures of causal strength; I argue that no single measure can satisfy all natural constraints; I prove two representation theorems for popular measures of causal strength: Pearl's causal effect measure and Eells' difference measure. In other words, I demonstrate these two measures can be derived from a set of plausible (...)
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  • Combining Versus Analyzing Multiple Causes: How Domain Assumptions and Task Context Affect Integration Rules.Michael R. Waldmann - 2007 - Cognitive Science 31 (2):233-256.
    In everyday life, people typically observe fragments of causal networks. From this knowledge, people infer how novel combinations of causes they may never have observed together might behave. I report on 4 experiments that address the question of how people intuitively integrate multiple causes to predict a continuously varying effect. Most theories of causal induction in psychology and statistics assume a bias toward linearity and additivity. In contrast, these experiments show that people are sensitive to cues biasing various integration rules. (...)
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  • The role of mechanism knowledge in singular causation judgments.Simon Stephan & Michael R. Waldmann - 2022 - Cognition 218 (C):104924.
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  • Causal Bayes nets as psychological theories of causal reasoning: evidence from psychological research.York Hagmayer - 2016 - Synthese 193 (4):1107-1126.
    Causal Bayes nets have been developed in philosophy, statistics, and computer sciences to provide a formalism to represent causal structures, to induce causal structure from data and to derive predictions. Causal Bayes nets have been used as psychological theories in at least two ways. They were used as rational, computational models of causal reasoning and they were used as formal models of mental causal models. A crucial assumption made by them is the Markov condition, which informally states that variables are (...)
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  • Inferring Hidden Causal Structure.Tamar Kushnir, Alison Gopnik, Chris Lucas & Laura Schulz - 2010 - Cognitive Science 34 (1):148-160.
    We used a new method to assess how people can infer unobserved causal structure from patterns of observed events. Participants were taught to draw causal graphs, and then shown a pattern of associations and interventions on a novel causal system. Given minimal training and no feedback, participants in Experiment 1 used causal graph notation to spontaneously draw structures containing one observed cause, one unobserved common cause, and two unobserved independent causes, depending on the pattern of associations and interventions they saw. (...)
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  • (1 other version)Mechanisms of theory formation in young children.Alison Gopnik - 2004 - Trends in Cognitive Sciences 8 (8):371-377.
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  • How causal knowledge simplifies decision-making.Rocio Garcia-Retamero & Ulrich Hoffrage - 2006 - Minds and Machines 16 (3):365-380.
    Making decisions can be hard, but it can also be facilitated. Simple heuristics are fast and frugal but nevertheless fairly accurate decision rules that people can use to compensate for their limitations in computational capacity, time, and knowledge when they make decisions [Gigerenzer, G., Todd, P. M., & the ABC Research Group (1999). Simple Heuristics That Make Us Smart. New York: Oxford University Press.]. These heuristics are effective to the extent that they can exploit the structure of information in the (...)
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  • BUCKLE: A model of unobserved cause learning.Christian C. Luhmann & Woo-Kyoung Ahn - 2007 - Psychological Review 114 (3):657-677.
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  • The meaning and computation of causal power: Comment on Cheng (1997) and Novick and Cheng (2004).Christian C. Luhmann & Woo-Kyoung Ahn - 2005 - Psychological Review 112 (3):685-692.
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  • Previous knowledge can induce an illusion of causality through actively biasing behavior.Ion Yarritu & Helena Matute - 2015 - Frontiers in Psychology 6.
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  • Postscript: Abandonment of Causal Power.Christian C. Luhmann & Woo-Kyoung Ahn - 2005 - Psychological Review 112 (3):692-693.
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