Written by one of the preeminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, economics, philosophy, cognitive science, and the health and social scienc...
Written by one of the preeminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, economics, philosophy, cognitive science, and the health and social sciences. Judea Pearl presents and unifies the probabilistic, manipulative, counterfactual, and structural approaches to causation and devises simple mathematical tools for studying the relationships between causal connections and statistical associations. Cited in more than 2,100 scientific publications, it continues to liberate scientists from the traditional molds of statistical thinking. In this revised edition, Judea Pearl elucidates thorny issues, answers readers' questions, and offers a panoramic view of recent advances in this field of research. Causality will be of interest to students and professionals in a wide variety of fields. Dr Judea Pearl has received the 2011 Rumelhart Prize for his leading research in Artificial Intelligence (AI) and systems from The Cognitive Science Society.
作者简介
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Judea Pearl is professor of computer science and statistics at the University of California, Los Angeles, where he directs the Cognitive Systems Laboratory and conducts research in artificial intelligence, human reasoning, and philosophy of science. The author of Heuristics and Probabilistic Reasoning, he is a member of the National Academy of Engineering and a Founding Fellow ...
Judea Pearl is professor of computer science and statistics at the University of California, Los Angeles, where he directs the Cognitive Systems Laboratory and conducts research in artificial intelligence, human reasoning, and philosophy of science. The author of Heuristics and Probabilistic Reasoning, he is a member of the National Academy of Engineering and a Founding Fellow of the American Association for Artificial Intelligence. Dr Pearl is the recipient of the IJCAI Research Excellence Award for 1999, the London School of Economics Lakatos Award for 2001, and the ACM Alan Newell Award for 2004. In 2008, he received the Franklin Medal for computer and cognitive science from the Franklin Institute.
目录
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1. Introduction to probabilities, graphs, and causal models;
2. A theory of inferred causation;
3. Causal diagrams and the identification of causal effects;
4. Actions, plans, and direct effects;
5. Causality and structural models in social science and economics;
6. Simpson's paradox, confounding, and collapsibility;
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(更多)
1. Introduction to probabilities, graphs, and causal models;
2. A theory of inferred causation;
3. Causal diagrams and the identification of causal effects;
4. Actions, plans, and direct effects;
5. Causality and structural models in social science and economics;
6. Simpson's paradox, confounding, and collapsibility;
7. The logic of structure-based counterfactuals;
8. Imperfect experiments: bounding effects and counterfactuals;
9. Probability of causation: interpretation and identification;
10. The actual cause.
· · · · · · (收起)
However, this corresponds to a general pattern of causal relationships: observations on a common consequence of two independent causes tend to render those causes dependent, because information about one of the causes tends to make the other more or less likely, given that the consequence has occurred. This pattern is known as selection bias or Berkson's paradox in the statistical literature (Berkson 1946) and as the explaining away effect in artificial intelligence. (Kim and Pearl 1983) (查看原文)
In view of this stability, it is no wonder that people prefer to encode knowledge in causal rather than probabilistic structures. Probabilistic relationships, such as marginal and conditional independencies, maybe helpful in hypothesizing initial causal structures from uncontrolled observations. However, once knowledge is cast in causal structure, those probabilistic relationships tend to be forgotten; whatever judgements people express about conditional independencies in a given domain are derived from the causal structure acquired. This explains why people feel confident asserting certain conditional independencies (e.g., that the price of beans in China is independent of the traffic in Los Angeles) having no idea whatsoever about the numerical probabilities involved (e.g., whether the p... (查看原文)
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机械工业出版社2022版
多来me发
机械工业出版社2022版
5 有用 ▽○▽ 2021-01-21 22:53:58
与其说是统计学/人工智能的书，不如说是哲学书。把这本书当作单纯介绍因果推断理论的专著是对Pearl思想精髓的断章取义，Pearl关注的不只是因果的问题，而且是关于人类认知推理的大问题。
3 有用 小闹钟 2023-06-06 01:44:52 美国
这应该叫一本哲学书了... 唯名论的自我扬弃..
1 有用 拖延的福娃 2014-12-25 15:57:32
讨论小组一起看了第一章。
1 有用 丁白 2022-06-20 11:53:11
看得出来大佬想探讨众多因果相关定义背后的直观背景，如条件独立性等等（涉及bayes的概率理解），但也因此丧失了部分严格性。对sem和反事实的内在原理阐述的极为清晰～
0 有用 己注销 2020-10-21 15:34:51
似醍醐灌顶
3 有用 小闹钟 2023-06-06 01:44:52 美国
这应该叫一本哲学书了... 唯名论的自我扬弃..
1 有用 丁白 2022-06-20 11:53:11
看得出来大佬想探讨众多因果相关定义背后的直观背景，如条件独立性等等（涉及bayes的概率理解），但也因此丧失了部分严格性。对sem和反事实的内在原理阐述的极为清晰～
0 有用 beren 2021-09-05 10:09:55
第一遍读，有挺多地方没读懂，大概弄明白了因果这套东西是做什么的，以及怎么做的，大概要边实践边思考了才能理解更深了
2 有用 wk 2021-02-27 11:40:59
大佬的教科书，不如他和人合写的那本清晰，这本有点啰嗦，当然也更全更细。
5 有用 ▽○▽ 2021-01-21 22:53:58
与其说是统计学/人工智能的书，不如说是哲学书。把这本书当作单纯介绍因果推断理论的专著是对Pearl思想精髓的断章取义，Pearl关注的不只是因果的问题，而且是关于人类认知推理的大问题。