The Book of Why: The New Science of Cause and EffectThe "extraordinary" (Science Friday), "illuminating" (New York Times) argument for 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. |
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The Book of Why: The New Science of Cause and Effect Judea Pearl,Dana Mackenzie No preview available - 2018 |
The Book of Why: The New Science of Cause and Effect Judea Pearl,Dana Mackenzie No preview available - 2018 |
Common terms and phrases
algorithm answer arrow artificial intelligence assumptions average back-door adjustment back-door path Bayesian networks belief propagation believe bias birth weight called causal diagram causal effect causal inference causal model Causal Revolution cause and effect Chapter cigarette collider conditional probability confounding correlation counterfactual deconfounders direct effect disease do-calculus do-operator do(X Door drug Epidemiology estimate example experiment fact factors Figure formula front-door Galton Greenland heart attack human indirect instrumental variables intervention intuition Journal of Epidemiology Judea Pearl Ladder of Causation linear lung cancer machine mathematical mediation analysis methods Monty Hall observed path analysis path coefficients path diagrams Pearl percent person philosophers population potential outcome predict problem query question R. A. Fisher randomized controlled trial reason regression researchers rung scientific scientists Sewall Wright Shpitser Simpson's paradox smoking gene statisticians statistics story tell tourniquet treatment uncertainty understand University