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Elements of causal inference : found...

Janzing, Dominik

Elements of causal inference : foundations and learning algorithms

紀錄類型:	書目-語言資料,印刷品 : 單行本
副題名:	foundations and learning algorithms
作者:	PetersJonas, 1984-
其他作者:	JanzingDominik,
其他作者:	SchölkopfBernhard,
出版地:	Cambridge, Mass.
出版者:	MIT Press;
出版年:	c2017
面頁冊數:	xiv, 265 p.ill. : 24 cm.;
集叢名:	Adaptive computation and machine learning
標題:	Causation -
標題:	Computer algorithms -
標題:	Inference -
標題:	Logic, Symbolic and mathematical -
標題:	Machine learning -
附註:	Includes bibliographical references and index
ISBN:	978-0-262-03731-0
內容註:	Machine generated contents note: 1.Statistical and Causal Models 1.1.Probability Theory and Statistics 1.2.Learning Theory 1.3.Causal Modeling and Learning 1.4.Two Examples 2.Assumptions for Causal Inference 2.1.The Principle of Independent Mechanisms 2.2.Historical Notes 2.3.Physical Structure Underlying Causal Models 3.Cause-Effect Models 3.1.Structural Causal Models 3.2.Interventions 3.3.Counterfactuals 3.4.Canonical Representation of Structural Causal Models 3.5.Problems 4.Learning Cause-Effect Models 4.1.Structure Identifiability 4.2.Methods for Structure Identification 4.3.Problems 5.Connections to Machine Learning, I 5.1.Semi-Supervised Learning 5.2.Covariate Shift 5.3.Problems 6.Multivariate Causal Models 6.1.Graph Terminology 6.2.Structural Causal Models 6.3.Interventions 6.4.Counterfactuals 6.5.Markov Property, Faithfulness, and Causal Minimality 6.6.Calculating Intervention Distributions by Covariate Adjustment 6.7.Do-Calculus 6.8.Equivalence and Falsifiability of Causal Models 6.9.Potential Outcomes 6.10.Generalized Structural Causal Models Relating Single Objects 6.11.Algorithmic Independence of Conditionals 6.12.Problems 7.Learning Multivariate Causal Models 7.1.Structure Identifiability 7.2.Methods for Structure Identification 7.3.Problems 8.Connections to Machine Learning, II 8.1.Half-Sibling Regression 8.2.Causal Inference and Episodic Reinforcement Learning 8.3.Domain Adaptation 8.4.Problems 9.Hidden Variables 9.1.Interventional Sufficiency 9.2.Simpson's Paradox 9.3.Instrumental Variables 9.4.Conditional Independences and Graphical Representations 9.5.Constraints beyond Conditional Independence 9.6.Problems 10.Time Series 10.1.Preliminaries and Terminology 10.2.Structural Causal Models and Interventions 10.3.Learning Causal Time Series Models 10.4.Dynamic Causal Modeling 10.5.Problems Appendices Appendix A Some Probability and Statistics A.1.Basic Definitions A.2.Independence and Conditional Independence Testing A.3.Capacity of Function Classes Appendix B Causal Orderings and Adjacency Matrices Appendix C Proofs C.1.Proof of Theorem 4.2 C.2.Proof of Proposition 6.3 C.3.Proof of Remark 6.6 C.4.Proof of Proposition 6.13 C.5.Proof of Proposition 6.14 C.6.Proof of Proposition 6.36 C.7.Proof of Proposition 6.48 C.8.Proof of Proposition 6.49 C.9.Proof of Proposition 7.1 C.10.Proof of Proposition 7.4 C.11.Proof of Proposition 8.1 C.12.Proof of Proposition 8.2 C.13.Proof of Proposition 9.3 C.14.Proof of Theorem 10.3 C.15.Proof of Theorem 10.4

Elements of causal inference : foundations and learning algorithms
Peters, Jonas

Elements of causal inference : foundations and learning algorithms / Jonas Peters, Dominik Janzing, and Bernhard Schölkopf - Cambridge, Mass. : MIT Press, c2017. - xiv, 265 p. ; ill. ; 24 cm.. - (Adaptive computation and machine learning).
Machine generated contents note: 1.Statistical and Causal Models.
Includes bibliographical references and index.
ISBN 978-0-262-03731-0
CausationComputer algorithmsInferenceLogic, Symbolic and mathematicalMachine learning

Janzing, Dominik

Elements of causal inference : foundations and learning algorithms
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