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

TitleThe book of why: the new science of cause and effect
Author
LanguageEnglish
File Size20.6 MB
Total Pages402
Table of Contents
                            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
                        
Document Text Contents
Page 201

FIGURE 6.9. Wainer and Brown’s revised version of Lord’s paradox and the

corresponding causal diagram.

Now that we have a thorough grounding in colliders, confounders, and the
perils that both pose, we are at last prepared to reap the fruits of our labor. In
the next chapter we begin our ascent up the Ladder of Causation, beginning
with rung two: intervention.

Page 202

Scaling “Mount Intervention.” The most familiar methods to estimate the effect of
an intervention, in the presence of confounders, are the back-door adjustment and
instrumental variables. The method of front-door adjustment was unknown before
the introduction of causal diagrams. The do-calculus, which my students have fully
automated, makes it possible to tailor the adjustment method to any particular

causal diagram. (Source: Drawing by Dakota Harr.)

Page 401

Treatise of Human Nature (Hume), 264–265, 265 (fig.)

Turing, Alan, 27, 29, 36–37, 108–109, 358

Tversky, Amos, 290

“Typical Laws of Heredity” (Galton), 54

uncertainty, 4, 109, 143

United States Department of Agriculture (USDA), 73

universal mapping tool, 219–220

VanderWeele, Tyler, 185, 342–343

Variables

causally relevant, 48–49

instrumental, 249–250, 249 (fig.), 257

in intervention, 257

pretreatment, 160

in probability, 48–49

Verma, Thomas, 87, 242, 245

Virgil, 3

vos Savant, Marilyn, 190–193, 191 (table), 196

Wainer, Howard, 216, 217 (fig.)

Wall, Melanie, 328, 331

weak AI, 362

weighted average, 106

Weinberg, Clarice, 162–163

Weinberg, Wilhelm, 65

Weissman, George, 177

Welling, David, 344

Wermuth, Nanny, 240–241, 241 (fig.)

Whig history, 65–66, 80

“Why?” question, 299–300, 349–350

Page 402

Wilcox, Allen, 186–187

Winship, Christopher, 115, 350

Wold, Herman, 244

would-haves, 329–336

Wright, Philip, 72, 250–252, 251 (fig.)

Wright, Sewall, 5–6, 18, 244, 309

on causation, 79–81

“Correlation and Causation” by, 82

on developmental factors, 74–76, 75 (fig.)

Fisher and, 85

guinea pigs of, 72–74, 74 (fig.), 222

on model-free approach, 88–89

Niles on, 78–81, 84

on path analysis, 86–89, 324

on path coefficients, 223, 251

path diagram of, 74–77, 75 (fig.), 85–86, 221, 260–261

Yerushalmy, Jacob, 167–169, 174, 183–184

Yule, George Udny, 68–72, 222

Zadeh, Lotfi, 109

Similer Documents