Elements of Causal Inference: Foundations and Learning Algorithms (Hardcover)
Jonas Peters, Dominik Janzing, Bernhard Schölkopf
- 出版商: MIT
- 出版日期: 2017-11-29
- 售價: $1,750
- 貴賓價: 9.5 折 $1,663
- 語言: 英文
- 頁數: 288
- 裝訂: Hardcover
- ISBN: 0262037319
- ISBN-13: 9780262037310
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相關分類:
Machine Learning、Algorithms-data-structures
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相關翻譯:
因果推理:基礎與學習算法 (簡中版)
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相關主題
商品描述
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.
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.
商品描述(中文翻譯)
一本簡潔且自成一體的引言,介紹因果推論,這在數據科學和機器學習中越來越重要。
因果性的數學化是近期的發展,並在數據科學和機器學習中變得越來越重要。本書提供了一個自成一體且簡潔的介紹,闡述了因果模型以及如何從數據中學習它們。
在解釋了因果模型的需求並討論了一些因果推論原則之後,本書教讀者如何使用因果模型:如何計算干預分佈,如何從觀察和干預數據中推斷因果模型,以及如何將因果思想應用於傳統機器學習問題。所有這些主題首先以兩個變量的形式進行討論,然後再進一步討論更一般的多變量情況。由於在解決多變量情況時,傳統方法所使用的條件獨立性不存在,因此雙變量情況被證明是因果學習的一個特別困難的問題。作者認為分析因果關係中的統計非對稱性非常有教育意義,並報告了他們十年來對這個問題進行的深入研究。
本書對具有機器學習或統計背景的讀者來說是易於理解的,可以用於研究生課程或作為研究人員的參考。該文本包含可以複製和粘貼的代碼片段、練習題和附錄,其中總結了最重要的技術概念。