The Book of Why: The New Science of Cause and Effect
A Turing Award-winning computer scientist and statistician shows how understanding causality has revolutionized science and will revolutionize artificial intelligence
"Correlation is not causation." This mantra, chanted by scientists for more than a century, 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. His 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. Pearl's work 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 key to artificial intelligence. Anyone who wants to understand either needs The Book of Why.
What people are saying - Write a review
We haven't found any reviews in the usual places.
Other editions - View all
algorithm answer arrow artificial intelligence assumptions average back-door criterion back-door path Bayes’s rule Bayesian networks belief propagation bias birth weight called causal diagram causal effect causal inference causal model Causal Revolution Chapter collider conditional probability confounding correlation counterfactual deconfounders definition direct effect disease do-calculus do-operator do(X door drug Epidemiology equations estimate example experiment explain fact factors Figure formula front-door Galton heart attack human indirect instrumental variables intervention intuition Journal Ladder of Causation language linear lung cancer machine mathematical means mediation analysis methods Monty Hall observed path analysis path coefficients path diagram Pearl percent person philosophers population potential outcome predict problem query R. A. Fisher randomized controlled trial reader reason regression researchers robot rung scientific scientists scurvy Sewall Wright Simpson’s paradox smoking gene statisticians statistics story strong AI tell tourniquet treatment understand vaccinated wrote