Title
Elements of Causal Inference: Foundations and Learning Algorithms (Adaptive Computation and Machine Learning series),Used
Sold by Ergodebooks, an authorized reseller.
Returns accepted within 30 days | support@ergodebooks.com
Shipping Information
- Free Standard Shipping — United States only
- Processing Time: 1–3 business days
- Estimated Delivery: 3–5 business days after dispatch
- Double-boxed, fully insured & discreetly packaged
- Tracking number sent via email once dispatched
- Orders over $250 require signature upon delivery. Taxes calculated at checkout.
Returns & Refund
Returns accepted within 30 days of delivery.
Damaged or Defective Item
Free return shipping + replacement or full refund
Wrong Item Received
Free return shipping + replacement or full refund
Change of Mind
Return shipping at customer's expense · 25% restocking fee applies
Payment Option
A concise and selfcontained 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 selfcontained 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.The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts.
⚠️ WARNING (California Proposition 65):
This product may contain chemicals known to the State of California to cause cancer, birth defects, or other reproductive harm.
For more information, please visit www.P65Warnings.ca.gov.
- Q: What is the main focus of 'Elements of Causal Inference'? A: The book focuses on providing a concise introduction to causal inference, emphasizing its growing importance in data science and machine learning.
- Q: Who is the author of this book? A: The author of 'Elements of Causal Inference' is Jonas Peters.
- Q: What topics are covered in this book? A: The book covers causal models, intervention distributions, causal inference from observational and interventional data, and the application of causal ideas in classical machine learning problems.
- Q: Is this book suitable for beginners? A: Yes, the book is accessible to readers with a background in machine learning or statistics, making it suitable for beginners and as a reference for researchers.
- Q: How many pages does the book contain? A: The book contains 288 pages.
- Q: What type of binding does this book have? A: The book is available in hardcover binding.
- Q: When was 'Elements of Causal Inference' published? A: The book was published on November 29, 2017.
- Q: Does the book include exercises or practical examples? A: Yes, the text includes exercises, code snippets, and an appendix summarizing important technical concepts.
- Q: Can this book be used for graduate courses? A: Yes, it can be used in graduate courses focused on causal inference and related topics.
- Q: What is the condition of the book being sold? A: The book is in new condition.