Summary of Judea Pearl and Dana Mackenzie's The Book of Why

By IRB Media

Get the Summary of Judea Pearl and Dana Mackenzie's The Book of Why in 20 minutes. Please note: This is a summary & not the original book. Original book introduction: "Correlation does not imply causation". This mantra has been invoked by scientists for decades and has led to a virtual prohibition on causal talk. But today, that taboo is dead. The causal revolution, sparked by Judea Pearl and his colleagues, has cut through a century of confusion and placed 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.

• Language: English
• Publisher: IRB Media
• Release date: Feb 3, 2021
• ISBN: 9781638152729

IRB Media

With IRB books, you can get the key takeaways and analysis of a book in 15 minutes. We read every chapter, identify the key takeaways and analyze them for your convenience.

Book preview

Summary

of

Judea Pearl’s

The Book of Why

The New Science of Cause and Effect

Table of Contents

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Important People

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Overview

The Book of Why: The New Science of Cause and Effect (2018) explores an underutilized science called causal inference, a tactic which mimics humanity’s natural ability to determine a cause by observing an effect. By employing causal inference, scientists are able to mathematically determine the causes and effects of a given phenomenon. With the help of science journalist Dana Mackenzie, renowned computer scientist Judea Pearl explains how he and his students at the University of California, Los Angeles developed models and mathematical language that can point toward causation, instead of just correlations. Even if researchers do not have data on all of the variables that might affect a given problem, they can use causal inference to create an informed estimate. By learning a mathematical language for causality, scientists will be able to prove cause and effect for complicated phenomena; they may even be able to devise theories without conducting experiments. Causal inference may also allow computer scientists and engineers to move one step closer to creating a machine that exhibits human intelligence.

Scientists first developed causal inference in the late nineteenth century, around the same time that modern statistics came into being as a scientific discipline.