Causal models. How People Think about the World and Its Alternatives

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, that is, between action and outcome. In cognitive terms, the question becomes one of how people construct and reason with the causal models we use to represent our world.

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

by Steven Sloman

ISBN-10: 0-19-518311-8

Publication date: 18 August 2005

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Description

• Investigates models that people to construct in order to understand cause and effect in their lives
• Includes perspectives from statisticians, philosophers, and computer scientists
• Describes a new framework for understanding causation based on probability and action

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, that is, between action and outcome. In cognitive terms, the question becomes one of how people construct and reason with the causal models we use to represent our world.

A revolution is occuring in how statisticians, philosophers, and computer scientists answer this question. These 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 question is not merely about probability (or logic), but about action. The framework offers a new understanding of mind: Thought is about the effects of intervention, so cognition is thereby intimately tied to actions that take place either in the actual physical world or in imagination, in counterfactual worlds.

In this book, Steven Sloman offers a conceptual introduction to the key mathematical ideas in the framework, presenting them in a non-technical way, by focusing on the intuitions rather than the theorems. He tries to show why the ideas are important to understanding how people explain things, and why it is so central to human action to think not only about the world as it is, but also about the world as it could be. Sloman also reviews the role of causality, causal models, and intervention in the basic human cognitive functions: decision making, reasoning, judgement, categorization, inductive inference, language, and learning. In short, this book offers a discussion about how people think, talk, learn, and explain things in causal terms – in terms of action and manipulation.

Readership: Cognitive psychologists, cognitive scientists, judgement/decision-making researchers, philosophers, computer scientists

Contents

• 1 Agency and the role of causation in mental life
• The High Church of Cognitive Science: A heretical view
• Agency is the ability to represent causal intervention
• The purpose of this book
• Plan of the book

Part I: The theory

• 2 The information is in the invariants
• Selective attention
• Selective attention focuses on invariants
• In the domain of events, causal relations are the fundamental invariants

• 3 What is a cause?
• Causes and effects are events
• Experiments versus observations
• Causal relations imply certain counterfactuals
• Enabling, disabling, directly responsible: Everything’s a cause
• Problems, problems
• Could it be otherwise?
• Not all variance is causal

• 4 Causal models
• The 3 parts of a causal model
• Independence
• Structural equations
• What does it mean to say causal relations are probabilistic?
• Causal structure produces a probabilistic world: Screening off
• Equivalent causal models
• The technical advantage: How to use a graph to simplify probabilities

• 5 Observation versus Action
• Seeing the representation of observation
• Action: The representation of intervention
• Acting and thinking by doing: Graphical surgery
• Computing with the do operator
• The value of experiments: A reprise
• The causal modeling framework and levels of causality

Part II: Evidence and application

• 6 Reasoning about causation
• Mathematical reasoning about causal systems
• Social attribution and explanation discounting
• Counterfactual reasoning: The logic of doing
• Conclusion

• 7 Decision making via Causal Consequences
• Making Decisions
• The gambling metaphor
• Deciding by causal explanation
• Newcomb’s Paradox: Causal trumps evidential expected utility
• The facts: People care about causal structure
• When causal knowledge isn’t enough

• 8 The psychology of judgement: Causality is pervasive
• Causal models as a psychological theory: Knowlege is qualitative
• The causality heuristic and mental stimulation
• Belief perseveration
• Seeing causality when it’s not there
• Causal models and legal relevance
• Conclusion

• 9 Causality and Conceptual Structure
• Inference over perception
• The role of function in artifact categorization
• Causal models of conceptual structure
• Some implications
• Causal versus other kinds of relations
• Basic-level categories and typical instances

• 10 Categorical Induction
• Induction and causal models
• Argument strength mediated by causal knowledge
• Causal analysis versus counting instances: The inside versus the outside
• Conclusion

• 11 Locating Causal Structure in Language
• Pronouns
• Conjunctions
• If
• The value of causal models

• 12 Causal Learning
• Covariation-based theories of causal learning
• Structure before strength
• Insufficency of covariational data
• Cues to causal structure
• Conclusion
• Conclusion

• 13 Causation in the mind
• Assessing the causal model framework
• Cognition is for action
• What causal models can contribute to human welfare
• The human mechanism