Elements of causal inference : foundations and learning algorithms
A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning.The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-containe...
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| Hlavní autori: | , , |
|---|---|
| Médium: | E-kniha Kniha |
| Jazyk: | English |
| Vydavateľské údaje: |
Cambridge
MIT Press
2017
The MIT Press |
| Vydanie: | 1 |
| Edícia: | Adaptive Computation and Machine Learning series |
| Predmet: | |
| ISBN: | 0262037319, 9780262037310 |
| On-line prístup: | Získať plný text |
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Obsah:
- 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 -- Bibliography -- Index -- Series Page
- Intro -- Series Announcement Page -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Preface -- Notation and Terminology -- 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