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

by Steven Sloman

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. 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 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. The 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 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.
"Sloman has written an accessible, popular-level book that will serve as a useful general introduction to the tricky and complex issues involved in understanding causality and its role in cognitive processing. For people who are unfamiliar with the issues and the research involved, this is a good starting point, although parts may require thoughtful rereadings. For people who are generally familiar with the issues but not the recent research or theoretical conceptions (e.g., the use of counterfactuals), this book can serve as a useful guide to update one's knowledge. People who are actively working in this area will probably find this book a quick and enjoyable read."--Michael Palij, PsycCRITIQUES
"The field of Bayesian causal models is becoming increasingly important for theory building in cognitive science. This book provides a lively and lucid introduction to the core concepts, and weaves them together with the latest research on causality and related topics from the cognitive sciences. Elegant and entertaining."--Nick Chater, Director of the Institute for Applied Cognitive Science and Professor of Psychology, University of Warwick
"The scientific analysis of causal systems has become much more sophisticated with recent developments in computer science, statistics, and philosophy during the past decade. For the first time, we have available a comprehensive formal language in which to represent complex causal systems and which can be used to define normative solutions to causal inference and judgment problems. Steven Sloman's book makes these important developments easily accessible to the reader, as well as presenting many of his own exciting applications of these new ideas in behavioral studies of learning and judging causal relationships. This well-written book is full of profound insights and fascinating results. Anyone who wants to know what's going on at the cutting edge of cognitive science should read it." --Reid Hastie, Professor of Behavioral Science, University of Chicago
"In the last 15 years, there has been a quiet revolution in how we model, understand, and learn about the causal structure of the world. Having started in philosophy and computer science, but now vital in psychology and statistics, the causal revolution has been slowed by the conspicuous absence of a truly readable book-length introduction. Fortunately, Steve Sloman has now written one. In a book that includes all the key ideas behind causal modeling but none of the tedious technical details, hundreds of worked examples ranging from marketing to arithmetic, and dozens of applications ranging from how we categorize the world to how we might be evolved to learn about its causal structure, Sloman has made a difficult subject exciting and simple." --Richard Scheines, Professor of Philosophy, Carnegie Mellon University
"Steven Sloman's Causal Models is the first broadly accessible book to survey an important and growing field of cognitive how people understand the causal structure of their world, and the role of causal understanding in all aspects of...

Genres: Psychology, Philosophy, Nonfiction, Academic

225 pages, ebook

First published January 1, 2005

Reviews

[Kevin · Rating 2 out of 5]

A clearly written introduction to the area of cognitive science which studies the type of logical/cognitive structure which arguably handles how we can intervene in the world. I would have found the book more useful had it included more links to other areas of brain science and/or more real-world examples. Nevertheless, it does suffice as a quick and compact introductory text.

[Tory S. Anderson · Rating 4 out of 5]

I used it as a proper to reading the definitive works on the subject by Judea Pearl. Unlike Pearl, this book emphasizes human thought and process with only a cursory overview of the mathematical formality. It is well-reasoned with excellent examples and clear points. In the end it did seem a little un-targeted, making a general "this is important stuff, worthy of attention" statement, but definitely insightful overall.

The subject focuses on the human plausability of causal models, models of the "why" of things, for how we think and make many of our decisions. Sloman didn't come up with the bulk if what he's talking about, but he is making strides in connecting it to human psychology.

[Henri Tournyol du Clos · Rating 2 out of 5]

This collection of tediously obvious banalities is somewhat painful to read and I never finished it. That is a pity, because Sloman can obviously do better: The Knowledge Illusion ( https://www.goodreads.com/book/show/3... ), also written for the general public, albeit a dozen years later, is a really insightful - if still somewhat at times watery - read.

[Prahlad Saldanha · Rating 4 out of 5]

The first half introduces the interventionist account of causality accessibly. The second half connects causality with other topics in cognitive psychology such as how we learn and how we understand language.

[Richard]

(I dunno — looks intriguing, but do I really need that deep of an education in Bayes’ theorem, for example? It should be mentioned that the single two-star review here actually reads more like a three or four star review, and the two more detailed reviews over on Amazon are four and five stars. Hmmm.)
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