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Why (Paperback)
Samantha Kleinberg
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R738
R545
Discovery Miles 5 450
Save R193 (26%)
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Ships in 12 - 17 working days
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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, identify causes, 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.
Causality is a key part of many fields and facets of life, from
finding the relationship between diet and disease to discovering
the reason for a particular stock market crash. Despite centuries
of work in philosophy and decades of computational research,
automated inference and explanation remains an open problem. In
particular, the timing and complexity of relationships has been
largely ignored even though this information is critically
important for prediction, explanation, and intervention. However,
given the growing availability of large observational datasets
including those from electronic health records and social networks,
it is a practical necessity. This book presents a new approach to
inference (finding relationships from a set of data) and
explanation (assessing why a particular event occurred), addressing
both the timing and complexity of relationships. The practical use
of the method developed is illustrated through theoretical and
experimental case studies, demonstrating its feasibility and
success.
Causality is a key part of many fields and facets of life, from
finding the relationship between diet and disease to discovering
the reason for a particular stock market crash. Despite centuries
of work in philosophy and decades of computational research,
automated inference and explanation remains an open problem. In
particular, the timing and complexity of relationships has been
largely ignored even though this information is critically
important for prediction, explanation, and intervention. However,
given the growing availability of large observational datasets
including those from electronic health records and social networks,
it is a practical necessity. This book presents a new approach to
inference (finding relationships from a set of data) and
explanation (assessing why a particular event occurred), addressing
both the timing and complexity of relationships. The practical use
of the method developed is illustrated through theoretical and
experimental case studies, demonstrating its feasibility and
success.
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