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Cited by 677Bayesian Modeling and Causal InferenceLogic, Reasoning, and KnowledgePhilosophy of Biology and Scientific Explanation

The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.

Causal Models: How People Think about the World and Its Alternatives

Sloman, Steven·2005
DOI 10.1093/acprof:oso/9780195183115.001.0001
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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.

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Metadata

Authors
Sloman, S.
Affiliations
Brown University (US)
Corresponding author
Sloman, Steven
ORCID
Sloman, Steven
Publisher
Oxford University PressNew York
Published
2005-08-18
DOI
10.1093/acprof:oso/9780195183115.001.0001
OpenAlex
W4211210448
Cited by
677
References
12
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 of Biology and Scientific Explanation
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

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Sloman, S. (2005). Causal Models: How People Think about the World and Its Alternatives. https://doi.org/10.1093/acprof%3Aoso/9780195183115.001.0001