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 ...
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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 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. 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 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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Add this copy of Causal Models: How People Think About the World and Its to cart. $6.00, very good condition, Sold by Academic Book Solutions rated 5.0 out of 5 stars, ships from Medford, NY, UNITED STATES, published 2009 by Oxford University Press, USA.
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In my view this is an interesting, lively written introductory book. If you want to know something about causal inference this could be a good place to start, but certainly not a good place to stop. By reading this book by itself you will NOT acquire the skill necesarry to use causal inference in practice - to this end you need to read something like Causality by Pearl or some of his papers (I'd say some of his papers are the best choice to know exactly what it all is about). There are two flavours of the general theory - Pearls SEM/graphical models framework and Rubin's potential outcome framework. Pearl himself prooved these are logically equivalent (the fact still ignored by some authors in the field) but Rubins framework is often much harder/less intuitive to work with, both because of the tricky nature of the basic notions used (potential outcome) and the lack of (natural) graphical language that makes Pearls approach more clear-cut. Anyway, this is a good read with many surprises and insights along the way.
