Causality is central to the understanding and use of data. Without an understanding of cause effect relationships, we cannot use data to answer questions as basic as, “Does this treatment harm or help patients?” But though hundreds of introductory texts are available on statistical methods of data analysis, until now, no beginner-level book has been written about the exploding arsenal of methods that can tease causal information from data.
Causal Inference in Statistics fills that gap. Using simple examples and plain language, the book lays out how to define causal parameters; the assumptions necessary to estimate causal parameters in a variety of situations; how to express those assumptions mathematically; whether those assumptions have testable implications; how to predict the effects of interventions; and how to reason counterfactually. These are the foundational tools that any student of statistics needs to acquire in order to use statistical methods to answer causal questions of interest.
This book is accessible to anyone with an interest in interpreting data, from undergraduates, professors, researchers, or to the interested layperson. Examples are drawn from a wide variety of fields, including medicine, public policy, and law; a brief introduction to probability and statistics is provided for the uninitiated; and each chapter comes with study questions to reinforce the readers understanding.
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因果推断是贝叶斯模型的一种功能，目前判别模型大行其道，可能不受推崇，但它是机器学习未来的发展方向。
评分读来挺有意思，有些东西（比如Simpson悖论）用数字说明了不代表真的明白其中的意思了。
评分内容太少太浅显了，对不起博大的书名。
评分读来挺有意思，有些东西（比如Simpson悖论）用数字说明了不代表真的明白其中的意思了。
评分????下辈子一定好好学 pearl 模型
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