Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Elements of Causal Inference: Foundations and Learning Algorithms

Elements of Causal Inference: Foundations and Learning Algorithms

Peters, Jonas
Janzing, Dominik
Scholkopf, Bernhard

Hardcover

Series: Adaptive Computation and Machine Learning

General ComputersProgramming

Currently unavailable to order

ISBN10: 0262037319
ISBN13: 9780262037310
Publisher: Mit Pr
Published: Nov 29 2017
Pages: 288
Weight: 1.50
Height: 0.90 Width: 7.20 Depth: 9.00
Language: English
A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning.

The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data.

After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem.

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