统计学中的因果推断
Causal Inference in Statistics
Judea Pearl、Nicholas P. Jewell · 初版 2016 年
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因果关系是理解与应用数据的核心。如果不理解因果关系，我们就无法利用数据回答像“这种疗法对患者是有害还是有益？”这样基础的问题。然而，尽管关于数据分析统计方法的入门教材已有成百上千本，但迄今为止，还没有一本书面向初学者系统介绍能够从数据中挖掘因果信息的丰富新方法。Causal Inference in Statistics 填补了这一空白。本书运用简单的实例和通俗的语言，阐述了如何定义因果参数；相关假设
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……写于2000年代初，里面满是定理+证明+详细阐述该理论所有主要成果的论述。如果你学过数学，它不算特别难，但它确确实实就是数学书。《统计中的因果推断》是一本2017年的书，它以一种平易近人的方式涵盖了上一本书中真正重要的成果，并侧重于将这些成果应用于实际问题。有些定理给出了形式化的陈述，……
…written in early 2000s, and is filled with theorems + proof + prose detailing all the major results from the theory. It's not a particularly hard book if you studied Mathematics, but it is Mathematics. "Causal inference in statistics" is a 2017 book that covers the actually important results from the previous book, in an approachable manner, and with a focus on applying the results to actual problems. Some theorems are stated formally,…
读读 Pearl/Glymour/Jewell 写的《统计中的因果推断：初阶教程》，你会受益匪浅（跟 Imbens 和 Rubin 的对比简直强烈得不可思议）
You will benefit from reading Pearl/Glymour/Jewell: Causal Inference in Statistics. A Primer (The contrast to Imbens and Rubin is crazy)
你可能会喜欢我的因果推断系列博客，里面我用 Python 梳理了 Pearl 的《统计中的因果推断：初阶教程》：https://github.com/DataForScience/Causality </厚颜无耻自我推销>
You might enjoy my blog series on Causality where I work through Pearls 'Causal Inference in Statistics: A Primer' using Python: https://github.com/DataForScience/Causality </ShamelessSelfPromotion>
还有《为什么：关于因果关系的新科学》，这是 Pearl 和 Dana Mackenzie 写的一本科普向的书。它包含的数学和例子恰到好处，让我对因果推断彻底着迷，所以我刚买了《统计中的因果推断》去深入研究理论细节。如果你想了解因果推断究竟是怎么回事，但又不想一上来就死磕教科书，我强烈推荐《为什么》。https://www.amazon.com/Book-Why-Science-Cause-Effect/dp/0465...
There's also "The Book of Why", which is a pop-sci-ish book by Pearl and Dana Mackenzie. It contained just enough math and examples to get me really excited for causal inference, so I just bought "Causal inference in statistics" to see the theory in detail. If you want to learn what causal inference is about, but don't necessarily want to wade through a textbook immediately, I highly recommend "The Book of Why". https://www.amazon.com/Book-Why-Science-Cause-Effect/dp/0465...
……建议。有一些东西我认为每个搞统计的人都应该读读，比如 Freedman 的《统计模型与鞋底皮》[^1] 和 Tukey 的《探索性数据分析》[^2]。Pearl 的《统计中的因果推断》是我所知道的、对于按当今理解的因果问题最好的入门书。至于实际推断，基础是单人博弈论（即决策论）。我是从 Kiefer 的《导论……
…suggest. There are some things that I think everyone who deals with statistics should read, such as Freedman's 'Statistical Models and Shoe Leather'[^1] and Tukey's 'Exploratory Data Analysis'[^2]. Pearl's 'Causal Inference in Statistics' is the best introduction I know of to issues of causality as we understand them today. For actual inference, the basis is one player game theory (aka, decision theory). I learned it from Kiefer's 'Introduction…
《统计中的因果推断：初阶教程》相对更轻量一些（至少对于有统计学背景的人来说是这样）。https://www.amazon.com/Causal-Inference-Statistics-Judea-Pea...
"Causal Inference in Statistics: A Primer" is somewhat lighter (at least if you have an statistics background). https://www.amazon.com/Causal-Inference-Statistics-Judea-Pea...