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
什么样的假设和方法可以让我们将观察结果转化为因果知识，以及不完整的因果知识如何能够用于规划和预测，以影响和控制我们的环境?
在这本书中，Peter spirtes, clark glymour 和 Richard scheines 使用贝叶斯网络的形式来解决这些问题，其结果已经应用于社会、行为和物理科学的不同研究领域。
作者指出，尽管实验和观察性研究设计可能不总是允许相同的推论，但它们遵循统一的原则。他们公理化了因果结构和概率独立性之间的联系，探索了几种不同的因果不可区分性，制定了一种操作理论，并开发了在因果模型的等价类中搜索的渐近可靠过程，包括有潜在变量和没有潜在变量的分类数据模型和结构方程模型。作者表明，因果关系和概率之间的关系也可以帮助澄清统计学中的不同主题，如：实验与观察的比较效力(power)，辛普森悖论，回归模型中的错误，回顾性与前瞻性抽样，以及变量选择。
第二版载有一个新的导言和对自1993年第一版出版以来出现的进展和应用的广泛调查。
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
1 Introduction and Advertisement
2 Formal Preliminaries
3 Causation and Prediction: Axioms and Explications
4 Statistical Indistinguishability
5 Discovery Algorithms for Causally Sufficient Structures
6 Discovery Algorithms without Causal Sufficiency
7 Prediction
8 Regression, Causation, and Prediction
9 The Design of Empirical Studies
10 The Structure of the Unobserved
11 Elaborating Linear Theories with Unmeasured Variables
12 Prequels and Sequels
13 Proofs of Theorems
资源获取
相关wiki
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