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[[文件:Causation, Prediction, and Search.jpg|替代=|缩略图|Causation, Prediction, and Search]]
=内容简介-英文=
What assumptions and methods allow us to turn observations into causal knowledge, and how can even incomplete causal knowledge be used in planning and prediction to influence and control our environment? In this book Peter Spirtes, Clark Glymour, and Richard Scheines address these questions using the formalism of Bayes networks, with results that have been applied in diverse areas of research in the social, behavioral, and physical sciences.The authors show that although experimental and observational study designs may not always permit the same inferences, they are subject to uniform principles. They axiomatize the connection between causal structure and probabilistic independence, explore several varieties of causal indistinguishability, formulate a theory of manipulation, and develop asymptotically reliable procedures for searching over equivalence classes of causal models, including models of categorical data and structural equation models with and without latent variables.The authors show that the relationship between causality and probability can also help to clarify such diverse topics in statistics as the comparative power of experimentation versus observation, Simpson's paradox, errors in regression models, retrospective versus prospective sampling, and variable selection. The second edition contains a new introduction and an extensive survey of advances and applications that have appeared since the first edition was published in 1993.
=内容简介-中文=
[[文件:《因果推理：基础与学习算法》.jpg|缩略图]]
=基本信息=
书名：Causation, Prediction, and Search
作者：Peter Spirtes / Clark Glymour / Richard Scheines
=作者介绍=
=内容目录=
=资源获取=
=相关wiki=
*[[因果推断 Causal inference]]
*[[因果涌现 Causal Emergence]]
*[[因果科学社区]]
=编者推荐=
[[File:因果推断.png|400px|right|thumb|[https://campus.swarma.org/course/1798 因果推理与机器学习读书会]]]
===集智俱乐部读书会推荐===
====[https://campus.swarma.org/course/1798 因果推理与机器学习读书会]====
大数据时代的下一场变革——因果革命正在酝酿之中，通过融合因果推理和机器学习而构建出来的Causal AI系统，有望奠定强人工智能的基石。集智俱乐部联合北京智源人工智能研究院，邀请了一批对因果科学与Casual AI感兴趣的研究者，开展为期2-3个月的系列线上读书会，研读经典和前沿论文，并尝试集体撰写一部书籍。
===集智俱乐部相关文章===
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====[https://swarma.org/?p=34072 第二种想象力：社会科学中的因果推断]====
:本文中南京大学陈云松教授从“因果”和“数据”两个维度，用因果推断、大数据和机器学习等方面的系列研究案例，阐释第二种想象力的八类思维面向。
====[https://pattern.swarma.org/article/145 因果科学的学习路线图]====
:本文主要根据对因果推断引擎的介绍，为关注因果科学领域的初学者提供了完整的学习路径
----本中文词条由因果读书会词条梳理志愿者[[用户:于沂渭|一尾鱼]]编辑，未经专家审核，带来阅读不便，请见谅。
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