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Copyright Copyright © 2018 by Judea Pearl and Dana Mackenzie Hachette Book Group supports the right to free expression and the value of copyright. The purpose of copyright is to encourage writers and artists to produce the creative works that enrich our culture. The scanning, uploading, and distribution of this book without permission is a theft of the author’s intellectual property. If you would like permission to use material from the book (other than for review purposes), please contact permissions@hbgusa.com. Thank you for your support of the author’s rights. Basic Books Hachette Book Group 1290 Avenue of the Americas, New York, NY 10104 www.basicbooks.com First Edition: May 2018 Published by Basic Books, an imprint of Perseus Books, LLC, a subsidiary of Hachette Book Group, Inc. The Basic Books name and logo is a trademark of the Hachette Book Group. The Hachette Speakers Bureau provides a wide range of authors for speaking events. To find out more, go to www.hachettespeakersbureau.com or call (866) 376-6591. The publisher is not responsible for websites (or their content) that are not owned by the publisher. Library of Congress Cataloging-in-Publication Data Names: Pearl, Judea, author. | Mackenzie, Dana, author. Title: The book of why : the new science of cause and effect / Judea Pearl and Dana Description: New York : Basic Books, [2018] | Includes bibliographical references and Mackenzie. index. Identifiers: LCCN 2017056458 (print) | LCCN 2018005510 (ebook) | ISBN 9780465097616 (ebook) | ISBN 9780465097609 (hardcover) | ISBN 046509760X (hardcover) | ISBN 0465097618 (ebook) Subjects: LCSH: Causation. | Inference. 4 3

Classification: LCC Q175.32.C38 (ebook) | LCC Q175.32.C38 P43 2018 (print) | DDC 501— dc23 LC record available at https://lccn.loc.gov/2017056458 ISBNs: 978-0-465-09760-9 (hardcover); 978-0-465-09761-6 (ebook) E3-20180417-JV-PC 5 4

CONTENTS Cover Title Page Copyright Dedication 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. Holmes 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 About the Authors Also by Judea Pearl Notes Bibliography Index 6 5

PREFACE ALMOST two decades ago, when I wrote the preface to my book Causality (2000), I made a rather daring remark that friends advised me to tone down. “Causality has undergone a major transformation,” I wrote, “from a concept shrouded in mystery into a mathematical object with well-defined semantics and well-founded logic. Paradoxes and controversies have been resolved, slippery concepts have been explicated, and practical problems relying on causal information that long were regarded as either metaphysical or unmanageable can now be solved using elementary mathematics. Put simply, causality has been mathematized.” Reading this passage today, I feel I was somewhat shortsighted. What I described as a “transformation” turned out to be a “revolution” that has changed the thinking in many of the sciences. Many now call it “the Causal Revolution,” and the excitement that it has generated in research circles is spilling over to education and applications. I believe the time is ripe to share it with a broader audience. This book strives to fulfill a three-pronged mission: first, to lay before you in nonmathematical language the intellectual content of the Causal Revolution and how it is affecting our lives as well as our future; second, to share with you some of the heroic journeys, both successful and failed, that scientists have embarked on when confronted by critical cause-effect questions. Finally, returning the Causal Revolution to its womb in artificial intelligence, I aim to describe to you how robots can be constructed that learn to communicate in our mother tongue—the language of cause and effect. This new generation of robots should explain to us why things happened, why they responded the way they did, and why nature operates one way and not another. More ambitiously, they should also teach us about ourselves: why our mind clicks the way it does and what it means to think rationally about cause and effect, credit and regret, intent and responsibility. When I write equations, I have a very clear idea of who my readers are. Not so when I write for the general public—an entirely new adventure for me. Strange, but this new experience has been one of the most rewarding educational trips of my life. The need to shape ideas in your language, to guess your background, your questions, and your reactions, did more to sharpen my understanding of causality than all the equations I have written prior to writing this book. For this I will forever be grateful to you. I hope you are as excited as I am to see the results. 8 Judea Pearl 6

Los Angeles, October 2017 9 7

INTRODUCTION: MIND OVER DATA Every science that has thriven has thriven upon its own symbols. —AUGUSTUS DE MORGAN (1864) THIS book tells the story of a science that has changed the way we distinguish facts from fiction and yet has remained under the radar of the general public. The consequences of the new science are already impacting crucial facets of our lives and have the potential to affect more, from the development of new drugs to the control of economic policies, from education and robotics to gun control and global warming. Remarkably, despite the diversity and apparent incommensurability of these problem areas, the new science embraces them all under a unified framework that was practically nonexistent two decades ago. The new science does not have a fancy name: I call it simply “causal inference,” as do many of my colleagues. Nor is it particularly high-tech. The ideal technology that causal inference strives to emulate resides within our own minds. Some tens of thousands of years ago, humans began to realize that certain things cause other things and that tinkering with the former can change the latter. No other species grasps this, certainly not to the extent that we do. From this discovery came organized societies, then towns and cities, and eventually the science- and technology-based civilization we enjoy today. All because we asked a simple question: Why? Causal inference is all about taking this question seriously. It posits that the human brain is the most advanced tool ever devised for managing causes and effects. Our brains store an incredible amount of causal knowledge which, supplemented by data, we could harness to answer some of the most pressing questions of our time. More ambitiously, once we really understand the logic behind causal thinking, we could emulate it on modern computers and create an “artificial scientist.” This smart robot would discover yet unknown phenomena, find explanations to pending scientific dilemmas, design new experiments, and continually extract more causal knowledge from the environment. But before we can venture to speculate on such futuristic developments, it is important to understand the achievements that causal inference has tallied thus far. We will explore the way that it has transformed the thinking of scientists in almost every data-informed discipline and how it is about to change our lives. The new science addresses seemingly straightforward questions like these: 10 8

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