Price: $45.00 – $37.00
(as of Dec 26,2024 14:53:20 UTC – Details)
Publisher : The MIT Press (November 29, 2017)
Language : English
Hardcover : 288 pages
ISBN-10 : 0262037319
ISBN-13 : 978-0262037310
Reading age : 18 years and up
Grade level : 12 and up
Item Weight : 1 pounds
Dimensions : 9 x 7.2 x 0.9 inches
Elements of Causal Inference: Foundations and Learning Algorithms (Adaptive Computation and Machine Learning series)
Causal inference is a crucial aspect of data analysis, allowing us to understand the relationships between variables and make informed decisions based on causal relationships rather than just correlations. In the book “Elements of Causal Inference: Foundations and Learning Algorithms,” authors Judea Pearl and Elias Bareinboim provide a comprehensive overview of the foundations and methods of causal inference.
The book covers key concepts such as causality, counterfactual reasoning, and the use of graphical models to represent causal relationships. It also discusses the challenges and limitations of causal inference, as well as the latest developments in the field.
One of the highlights of the book is its focus on learning algorithms for causal inference. The authors provide detailed explanations of the most commonly used methods for estimating causal effects, including propensity score matching, instrumental variables, and structural equation modeling.
Whether you are a researcher, data scientist, or machine learning enthusiast, “Elements of Causal Inference” is a must-read for anyone interested in understanding causal relationships and making better decisions based on data. This book is part of the Adaptive Computation and Machine Learning series, making it an essential resource for anyone working in the field of machine learning and artificial intelligence.
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