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Elements Of Causal Inference: Foundations And Learning Algorithms (Adaptive Computation And Machine Learning Series),New
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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.
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- Q: How many pages does this book have? A: This book has two hundred eighty-eight pages. It provides a comprehensive overview of causal inference, making it a valuable resource.
- Q: What are the dimensions of this book? A: The dimensions of this book are nine point zero two inches in length, seven point two inches in width, and zero point ninety-one inches in height.
- Q: What is the binding type of this book? A: This book is hardcover bound. This durable binding method is suitable for repeated use and longevity.
- Q: Who is the author of this book? A: The author of this book is Jonas Peters. He is known for his contributions to the field of causal inference in machine learning.
- Q: What is the main focus of this book? A: This book focuses on causal inference and learning algorithms. It is aimed at those interested in data science and machine learning.
- Q: Is this book suitable for beginners? A: Yes, this book is suitable for readers with a background in machine learning or statistics. It serves as both an introduction and a reference guide.
- Q: Can this book be used in graduate courses? A: Yes, this book can be used in graduate courses. It is designed to be both educational and practical for advanced studies.
- Q: Does this book include exercises? A: Yes, this book includes exercises. These are designed to reinforce learning and application of causal inference concepts.
- Q: Are there any coding examples in this book? A: Yes, the book includes code snippets. Readers can copy and paste these snippets for practical application.
- Q: What topics are covered in this book? A: The book covers causal models, intervention distributions, and causal learning from data. It includes both bivariate and multivariate cases.
- Q: How should I care for this book? A: To care for this book, keep it in a dry, cool place. Avoid exposing it to direct sunlight to maintain its condition.
- Q: Is this book safe for children? A: This book is aimed at an adult audience. It may not be suitable for young children due to its complex subject matter.
- Q: What if my book arrives damaged? A: If your book arrives damaged, contact customer support for assistance. They will guide you through the return or exchange process.
- Q: Can I return this book if I don’t like it? A: Yes, you can return this book within the specified return window. Make sure to keep the original packaging for the return.
- Q: What if I have trouble understanding the content? A: If you have trouble understanding the content, consider joining study groups or seeking online resources. Additional explanations can enhance comprehension.
- Q: Is there a warranty for this book? A: Typically, books do not come with a warranty. However, check with the seller for any specific return policies or guarantees.