Causal Models

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.

Steven Sloman has been on the faculty in Cognitive and Linguistic Sciences at Brown University since 1992. He completed his undergraduate studies at the University of Toronto in 1986 and received a Ph.D. in Psychology from Stanford in 1990. He has published many papers and a book about human cognition on topics ranging from categorization and memory to decision-making, inductive inference, and reasoning.

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 1: 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 2: 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