Causal Models: How People Think about the World and Its Alternatives
Abstract
Human beings are active agents who can think. To understand how thought serves action requires understanding how people conceive of the relation between cause and effect, between action and outcome. This book presents the question, in cognitive terms: how do people construct and reason with the causal models we use to represent our world? A revolution is occurring in how statisticians, philosophers, and computer scientists answer this question. Those fields have ushered in new insights about causal models by thinking about how to represent causal structure mathematically, in a framework that uses graphs and probability theory to develop what are called causal Bayesian networks. The framework starts with the idea that the purpose of causal structure is to understand and predict the effects of intervention. How does intervening on one thing affect other things? This is not a question merely about probability (or logic), but about action. The framework offers a new understanding of mind: thought is about the effects of intervention and cognition is thus intimately tied to actions that take place either in the actual physical world or in imagination, in counterfactual worlds. This book offers a conceptual introduction to the key mathematical ideas, presenting them in a non-technical way, focusing on the intuitions rather than the theorems. It tries to show why the ideas are important to understanding how people explain things and why thinking not only about the world as it is but the world as it could be is so central to human action. The book reviews the role of causality, causal models, and intervention in the basic human cognitive functions: decision making, reasoning, judgment, categorization, inductive inference, language, and learning. In short, the book offers a discussion about how people think, talk, learn, and explain things in causal terms, in terms of action and manipulation.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Causal junctures drive meaning making by indicating which information is relevant.
“Another way that causal junctures drive meaning making is by indicating which information is relevant (Sloman, 2005).”
From Stories in Action. (2022) · cites this paper as Sloman, 2005
Cites
The Ecological Approach to Visual Perception
Bornstein, M. H., & Gibson, J. J. (1980) · The Journal of Aesthetics and Art Criticism · doi:10.2307/429816 · Cited by 22,935 · see citing works
I.—COMPUTING MACHINERY AND INTELLIGENCE
TURING, A. M. (1950) · Mind · doi:10.1093/mind/lix.236.433 · Cited by 10,290 · see citing works
Fodor, J. A. (1983) · doi:10.7551/mitpress/4737.001.0001 · Cited by 7,734 · see citing works
REMEMBERING: A STUDY IN EXPERIMENTAL AND SOCIAL PSYCHOLOGY
Bartlett, F. C., & Burt, C. (1933) · British Journal of Educational Psychology · doi:10.1111/j.2044-8279.1933.tb02913.x · Cited by 7,475 · see citing works
An Experimental Study of Apparent Behavior
Heider, F., & Simmel, M. (1944) · The American Journal of Psychology · doi:10.2307/1416950 · Cited by 3,340 · see citing works
Confirmation, disconfirmation, and information in hypothesis testing
Klayman, J., & Ha, Y.-W. (1987) · Psychological Review · doi:10.1037/0033-295x.94.2.211 · Cited by 2,094 · see citing works
Concepts, Kinds, and Cognitive Development
Keil, F. C. (1989) · doi:10.7551/mitpress/2065.001.0001 · Cited by 1,886 · see citing works
Jones, E. E., & Harris, V. A. (1967) · Journal of Experimental Social Psychology · doi:10.1016/0022-1031(67)90034-0 · Cited by 1,783 · see citing works
Cheng, P. W., & Holyoak, K. J. (1985) · Cognitive Psychology · doi:10.1016/0010-0285(85)90014-3 · Cited by 1,463 · see citing works
Fault trees: sensitivity of estimated failure probabilities to problem representation
Fischhoff, B., Slovic, P., & Lichtenstein, S. (1978) · Journal of Experimental Psychology: Human Perception and Performance · doi:10.1037/0096-1523.4.2.330 · Cited by 708 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Building Better Causal Theories: A Fuzzy Set Approach to Typologies in Organization Research
Fiss, P. C. (2011) · Academy of Management Journal · doi:10.5465/amj.2011.60263120 · Cited by 5,930 · see citing works
The probability of causal conditionals
Over, D. E., Hadjichristidis, C., Evans, J. S. B. T., Handley, S. J., & Sloman, S. A. (2007) · Cognitive Psychology · doi:10.1016/j.cogpsych.2006.05.002 · Cited by 229 · see citing works
Self-citationThe power of possibility: causal learning, counterfactual reasoning, and pretend play
Buchsbaum, D., Bridgers, S., Skolnick Weisberg, D., & Gopnik, A. (2012) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2012.0122 · Cited by 200 · see citing works
Formalizing Neurath’s ship: approximate algorithms for online causal learning.
Bramley, N. R., Dayan, P., Griffiths, T. L., & Lagnado, D. A. (2017) · Psychological Review · doi:10.1037/rev0000061 · Cited by 136 · see citing works
Williams, D. (2020) · Synthese · doi:10.1007/s11229-018-1768-x · Cited by 63 · see citing works
Explainable Reinforcement Learning through a Causal Lens
Madumal, P., Miller, T., Sonenberg, L., & Vetere, F. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i03.5631 · Cited by 58 · see citing works
Walker, C. M., & Gopnik, A. (2013) · Psychological Bulletin · doi:10.1037/a0030151 · Cited by 40 · see citing works
Hagmayer, Y., & Engelmann, N. (2014) · Frontiers in Psychology · doi:10.3389/fpsyg.2014.01303 · Cited by 38 · see citing works
Walsh, J., Vaida, N., Coman, A., & Fiske, S. T. (2022) · Psychological Science in the Public Interest · doi:10.1177/15291006231161337 · Cited by 26 · see citing works
“Another way that causal junctures drive meaning making is by indicating which information is relevant (Sloman, 2005).”
BackgroundUnit Testing for Concepts in Neural Networks
Lovering, C., & Pavlick, E. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00514 · Cited by 12 · see citing works
Active disambiguation guides inferring controllability and cause in social interactions
Spiering, L., Trier, H. A., O’Reilly, J. X., Kolling, N., Wittmann, M. K., Rushworth, M. F. S., & Scholl, J. (2025) · Nature Communications · doi:10.1038/s41467-025-67853-8 · Cited by 2 · see citing works
- Authors
- Sloman, S.
- Affiliations
- Brown University (US)
- Corresponding author
- Sloman, Steven
- ORCID
- Sloman, Steven
- Publisher
- Oxford University PressNew York
- Published
- 2005-08-18
- OpenAlex
- W4211210448
- Cited by
- 677
- References
- 10
- Citations by year
- 2026: 19 · 2025: 29 · 2024: 60 · 2023: 38 · 2022: 41 · 2021: 47 · 2020: 29 · 2019: 34 · 2018: 32 · 2017: 34 · 2016: 37 · 2015: 30 · 2014: 42 · 2013: 35 · 2012: 35
- FWCI
- 6.47
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy and History of Science
- Keywords
- causal models, causal Bayesian networks, causal reasoning, counterfactual reasoning, intervention, causal structure, causal explanation, decision-making, judgment, categorization, inductive reasoning, language, learning, cognition and action
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
