Scholkopf, Bernhard
Elements of Causal Inference: Foundations and Learning Algorithms (Adaptive Computation and Machine Learning series)
- ISBN 13:
- 9780262037310
- author:
- Scholkopf, Bernhard
- format:
- HardBack
- publisher:
- MIT Press
- language:
- English
- Publication Year:
- 2017
- Pages:
- 288
- Dimensions:
- 22.86 x 18.29 x 2.29 centimetres
- Genre:
- Computers, Programming, General
- Condition:
- New
- Availability:
- Item usually sent within 10 working days
Description
Elements of Causal Inference: Foundations and Learning Algorithms by Jonas Peters, Dominik Janzing, and Bernhard Scholkopf provides a concise introduction to causal inference, a crucial concept in data science and machine learning. This self-contained book explains the mathematization of causality and how to learn causal models from data. The authors discuss the need for causal models, principles underlying causal inference, and how to use them to solve problems in classical machine learning. They also explore statistical asymmetries between cause and effect, a topic they have researched extensively over a decade. The book includes code snippets, exercises, and an appendix with technical concepts, making it accessible to readers with a background in machine learning or statistics. Suitable for graduate courses or as a reference for researchers, this book offers practical insights into causal inference and its applications in data science and machine learning.
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