Can drinking coffee help people live longer? What makes a stock's price go up? Why did you get the flu? Causal questions like these arise on a regular basis, but most people likely have not thought deeply about how to answer them.
This book helps you think about causality in a structured way: What is a cause, what are causes good for, and what is compelling evidence of causality? Author Samantha Kleinberg shows you how to develop a set of tools for thinking more critically about causes. You'll learn how to question claims, explain the past, make decisions based on causal information, and verify causes through further tests.
Whether it's figuring out what data you need, or understanding that the way you collect and prepare data affects the conclusions you can draw from it, Why will help you sharpen your causal inference skills.
- "While cutting-edge computing tools make it easy to find patterns in data, the best insights come from understanding where those patterns come from, and this problem can't be solved by computers alone. Kleinberg expertly guides readers on a tour of the key concepts and methods for identifying causal relationships, with a clear and practical approach that makes Why unlike any other book on the subject. Accessible yet comprehensive, Why is essential reading for scientific novices, seasoned experts, and anyone else looking to learn more from data."
—Andrew Therriault, Director of Data Science, Democratic National Committee
- "Philosophy, economics, statistics, and logic all try to make sense of causality; Kleinberg manages to tie together these disparate approaches in a way that's straightforward and practical. As more of our lives become "data driven," clear thinking about inferring causality from observations will be needed for understanding policy, health, and the world around us."
—Chris Wiggins, Chief Data Scientist at the New York Times and Associate Professor at Columbia University
- "While causality is a central feature of our lives, there is widespread debate and misunderstanding about it. Why lucidly explains causality without relying on prior knowledge or technical expertise. It is an accessible and enjoyable read, yet it gives logical rigour and depth of analysis to complex concepts."
—David Lagnado, University College London
- "...a necessary addition to any data scientist’s bookshelf as it helps bring focus to the dreaded "correlation does not imply causation" conundrum that affects our understanding of data-centric problems."
—Daniel D. Gutierrez, insideBIGDATA