The book of why : the new science of cause and effect

The book of why The new science of cause and effect

Judea Pearl

Book - 2018

"Everyone has heard the claim, "Correlation does not imply causation." What might sound like a reasonable dictum metastasized in the twentieth century into one of science's biggest obstacles, as a legion of researchers became unwilling to make the claim that one thing could cause another. Even two decades ago, asking a statistician a question like "Was it the aspirin that stopped my headache?" would have been like asking if he believed in voodoo, or at best a topic for conversation at a cocktail party rather than a legitimate target of scientific inquiry. Scientists were allowed to posit only that the probability that one thing was associated with another. This all changed with Judea Pearl, whose work on causal...ity was not just a victory for common sense, but a revolution in the study of the world"--

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Subjects
Causation.
Inference.
Published
New York : Basic Books 2018.
Language
English
Main Author
Judea Pearl (author)
Other Authors
Dana Mackenzie (author)
Edition
First edition
Physical Description
x, 418 pages ; 25 cm
Bibliography
Includes bibliographical references and index.
ISBN
9780465097609
  • Preface
  • Introduction Mind over Data
  • Chapter 1. The Ladder of Causation
  • Chapter 2. From Buccaneers to Guinea Pigs: The Genesis of Causal Inference
  • Chapter 3. From Evidence to Causes: Reverend Bayes Meets Mr
  • Chapter 4. Confounding and Deconfounding: Or, Slaying the Lurking Variable
  • Chapter 5. The Smoke-Filled Debate: Clearing the Air
  • Chapter 6. Paradoxes Galore!
  • Chapter 7. Beyond Adjustment: The Conquest of Mount Intervention
  • Chapter 8. Counterfactuals: Mining Worlds That Could Have Been
  • Chapter 9. Mediation: The Search for a Mechanism
  • Chapter 10. Big Data, Artificial Intelligence, and the Big Questions
  • Acknowledgments
  • Notes
  • Bibliography
  • Index
Review by Choice Review

Statisticians have long repeated the mantra that correlation is not causality. The philosopher Hans Reichenbach countered: no causality without correlation. Pearl, a prominent computer scientist (UCLA) and expert on artificial intelligence, connects these two using path diagrams to illustrate which factors determine true causal connections. The most interesting chapters deal with familiar paradoxes and their solutions from this viewpoint--including Pearl's surprising, perhaps counterintuitive explanation of the "Monty Hall" or "Lets-Make-a-Deal" paradox. This example serves to explain significant correlations between smoking, tar, and various illnesses, as well as "good" versus "bad" cholesterol and their relation to heart attacks. Also discussed is the process of predicting results of actions that haven't been tested, such as with medical trials, and forecasting the future of climate change. Then: what would be required to enable machines to think like humans? An ability to deal with intent and free will, for one; this cannot result from simply following instructions in a stored program. Will it be possible to create machines that are capable of distinguishing good from evil--or "moral" robots? Anyone interested in probing connections between cause and effect, and their relevance for the future of AI, will find this a fascinating and provocative book. Summing Up: Highly recommended. All readers.--Joseph W. Dauben, CUNY Herbert H. Lehman College

Copyright American Library Association, used with permission.

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