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Causal Learning

Causal Learning

by Alison Gopnik

First published 2007 · 358 pages · about 6 hours to read

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About this book

Causal Learning is a 2007 academic volume, edited by Alison Gopnik and Laura Schulz, that collects work on how people and other minds work out cause and effect. It brings together developmental and cognitive psychology, philosophy of science, and computational modelling. Researchers and advanced students read it for its account of how learning from observation and intervention can be formalised.

The book asks how learners, especially young children, infer which events cause which others, and what kind of mental machinery makes that possible. It treats causal reasoning as a central problem for psychology, philosophy and computer science.

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Will you like it?

Causal Learning suits researchers, graduate students and advanced readers in psychology, philosophy and cognitive science who want the state of the field on how people infer cause and effect. It is an edited collection of technical chapters, so it rewards readers who already know the experimental and modelling literature.

  • causal inference
  • children as learners
  • probabilistic models
  • philosophy of causation

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Quick answers

What age is Causal Learning for?
Adults.
Is Causal Learning a standalone?
Yes, it stands on its own.
Does Causal Learning have violence, sex or strong language?
Nothing a reader needs warning about.

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Reader's guide

Themescausal inference, children as learners, probabilistic models, interventions and observation, philosophy of causation

causal inference · children as learners · probabilistic models · interventions and observation · philosophy of causation

StyleAn edited academic collection of chapters by different contributors, written in the technical prose of experimental psychology and formal modelling. It assumes familiarity with the research literature and is not written for a general audience.

An edited academic collection of chapters by different contributors, written in the technical prose of experimental psychology and formal modelling. It assumes familiarity with the research literature and is not written for a general audience.

How it was writtenEdited by Alison Gopnik, a developmental psychologist at the University of California, Berkeley, with Laura Schulz, and published by Oxford University Press in 2007. It is an edited volume rather than a single-author book, and it reflects a period when researchers were using Bayesian and graphical models to study human learning.

Edited by Alison Gopnik, a developmental psychologist at the University of California, Berkeley, with Laura Schulz, and published by Oxford University Press in 2007. It is an edited volume rather than a single-author book, and it reflects a period when researchers were using Bayesian and graphical models to study human learning.

How it was receivedUnsure. It is generally cited in later work on causal cognition in children and adults, but I cannot describe its specific reception with confidence.

Unsure. It is generally cited in later work on causal cognition in children and adults, but I cannot describe its specific reception with confidence.

Questions to discussHow much of causal learning can be explained by statistical patterns in observed data, and how much needs prior assumptions?

These may talk about the whole book.

  1. How much of causal learning can be explained by statistical patterns in observed data, and how much needs prior assumptions?
  2. What does a child gain from intervening on the world compared with simply watching it?
  3. Do formal models of causal inference describe how minds work, or only how an ideal learner would work?
  4. How should philosophers and psychologists divide the work of explaining causation?
  5. What would a machine need in order to learn causes the way a young child does?
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