ALPHABETICAL BRAIN™ VOCABULARY
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OF SECULAR SCIENCE STARS
PEARL & MACKENZIE
June 8, 2020


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THE BOOK OF WHY
The New Science of Cause and Effect
by Judea Pearl & Dana Mackenzie
Basic Books, 2018 (i-x, 418 pages)

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BOOK OUTLINE
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PREFACE (ix-x)

INTRODUCTION Mind over data (pages 1-21)

Quote = "Every science that has thriven has
thriven upon its own symbols." by Augustus de Morgan in 1864 (page 1)

note = What is the "Causal Revolution" as it answers the hardest problems ever asked about cause-effect relationships." (page 7)

note = A blueprint of reality (pages 11-21)

1) THE LADDER OF CAUSATION (pages 23-51)

note = Photo of the earliest known representation of an imaginary creature: namely The Lion Man of Stadel Cave [Half man and half lion] (page 35)

2) FROM BUCCANEERS TO GUINEA PIGS THE GENESIS OF CAUSAL INFERENCE (pages 53-91)

note = illustration of Francis Galton who created a pinball-like apparatus as an analogy for the inheritance of genetic traits like stature. This led him to the discovery of "regression to the mean." (page 52)

Quote = "And yet it moves." attributed to Galileo Galilei who lived between 1564 and 1642. (page 52)

note = Use the last 3 paragraphs on page 90 and some on page 91 to explain Bayesian statistics (90-91)

3) FROM EVIDENCE TO CAUSES REVEREND BAYES MEETS MR. HOLMES (93-133)

note = (pages 128-133)

4) CONFOUNDING AND DE-CONFOUNDING OR, SLAYING THE LURKING VARIABLE (135-165)

5) THE SMOKE-FILLED DEBATE CLEARING THE AIR (167-187)

6) PARADOXES GALORE! (189-217)

7) BEYOND ADJUSTMENT THE CONQUEST OF MOUNT INTERVENTION (219-257)

8) COUNTERFACTUALS MINING WORLDS THAT COULD HAVE BEEN (259-297)

note = Robert Frost (258)

9) MEDIATION THE SEARCH FOR A MECHANISM (299-347)

10) BIG DATA, ARTIFICIAL INTELLIGENCE, AND THE BIG QUESTIONS (349-370)

ACKNOWLEDGMENTS (371-372)

NOTES (373-376)

BIBLIOGRAPHY (377-404)

INDEX P. 405-418)

BIOGRAPHICAL SKETCHES AND PICTURES AUTHORS (unnumbered at end)

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AUTHOR NOTE, SUMMARY,
AND BOOK DESCRIPTION

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AUTHOR NOTE = Judea Pearl and science writer, Dana Mackenzie,

SUMMARY = Start asking the big questions and learn how the study of causality revolutionized science and the world. A Turing Award-winning computer scientist and a statistician-science writer descdribe how understanding causality will revolutionize artificial intelligence.

BOOK DESCRIPTION = This mantra, "Correlation is not causation" has been chanted by scientists for more than a century. It has led to a virtual prohibition on causal talk. Today, that taboo is dead. The causal revolution, instigated by Judea Pearl and his colleagues, has cut through a century of confusion and established causality --- the study of cause and effect --- on a firm scientific basis.

Pearl's work explains how we can know easy things, like whether it was rain or a sprinkler that made a sidewalk wet; and how to answer hard questions, like whether a drug cured an illness. The book enables us to know not just whether one thing causes another: it lets us explore the world that is and the worlds that could have been. It shows us the essence of human thought and also is the key to understanding artificial intelligence. Anyone who wants to understand these subjects needs to read this book.

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EDITORIAL BOOK REVIEW
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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.

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BOOK REVIEW HIGHLIGHTS
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[1] Illuminating... a valuable lesson on the history of ideas. -- New York Times

[2] This book really gets you thinking about cause and effect as it applies to issues of our time... Extraordinary. -- Science Friday.

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