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Causal Inference in Statistics : A Primer.

By: Pearl, Judea.
Material type: materialTypeLabelBookSeries: New York Academy of Sciences Ser: Publisher: Newark : John Wiley & Sons, Incorporated, 2016Copyright date: �2016Description: 1 online resource (182 pages).Content type: text Media type: computer Carrier type: online resourceISBN: 9781119186861.Genre/Form: Electronic books.Online resources: Click to View
Contents:
Intro -- Title Page -- Copyright -- Dedication -- Table of Contents -- About the Authors -- Preface -- Acknowledgments -- List of Figures -- About the Companion Website -- Chapter 1: Preliminaries: Statistical and Causal Models -- 1.1 Why Study Causation -- 1.2 Simpson's Paradox -- 1.3 Probability and Statistics -- 1.4 Graphs -- 1.5 Structural Causal Models -- Bibliographical Notes for Chapter 1 -- Chapter 2: Graphical Models and Their Applications -- 2.1 Connecting Models to Data -- 2.2 Chains and Forks -- 2.3 Colliders -- 2.4 d-separation -- 2.5 Model Testing and Causal Search -- Bibliographical Notes for Chapter 2 -- Chapter 3: The Effects of Interventions -- 3.1 Interventions -- 3.2 The Adjustment Formula -- 3.3 The Backdoor Criterion -- 3.4 The Front-Door Criterion -- 3.5 Conditional Interventions and Covariate-Specific Effects -- 3.6 Inverse Probability Weighing -- 3.7 Mediation -- 3.8 Causal Inference in Linear Systems -- Bibliographical Notes for Chapter 3 -- Chapter 4: Counterfactuals and Their Applications -- 4.1 Counterfactuals -- 4.2 Defining and Computing Counterfactuals -- 4.3 Nondeterministic Counterfactuals -- 4.4 Practical Uses of Counterfactuals -- 4.5 Mathematical Tool Kits for Attribution and Mediation -- Bibliographical Notes for Chapter 4 -- References -- Index -- End User License Agreement.
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Item type Current location Collection Call number URL Copy number Status Date due Item holds
E-book E-book IUKL Library
Subscripti https://ebookcentral.proquest.com/lib/kliuc-ebooks/detail.action?docID=7104473 1 Available
Total holds: 0

Intro -- Title Page -- Copyright -- Dedication -- Table of Contents -- About the Authors -- Preface -- Acknowledgments -- List of Figures -- About the Companion Website -- Chapter 1: Preliminaries: Statistical and Causal Models -- 1.1 Why Study Causation -- 1.2 Simpson's Paradox -- 1.3 Probability and Statistics -- 1.4 Graphs -- 1.5 Structural Causal Models -- Bibliographical Notes for Chapter 1 -- Chapter 2: Graphical Models and Their Applications -- 2.1 Connecting Models to Data -- 2.2 Chains and Forks -- 2.3 Colliders -- 2.4 d-separation -- 2.5 Model Testing and Causal Search -- Bibliographical Notes for Chapter 2 -- Chapter 3: The Effects of Interventions -- 3.1 Interventions -- 3.2 The Adjustment Formula -- 3.3 The Backdoor Criterion -- 3.4 The Front-Door Criterion -- 3.5 Conditional Interventions and Covariate-Specific Effects -- 3.6 Inverse Probability Weighing -- 3.7 Mediation -- 3.8 Causal Inference in Linear Systems -- Bibliographical Notes for Chapter 3 -- Chapter 4: Counterfactuals and Their Applications -- 4.1 Counterfactuals -- 4.2 Defining and Computing Counterfactuals -- 4.3 Nondeterministic Counterfactuals -- 4.4 Practical Uses of Counterfactuals -- 4.5 Mathematical Tool Kits for Attribution and Mediation -- Bibliographical Notes for Chapter 4 -- References -- Index -- End User License Agreement.

Description based on publisher supplied metadata and other sources.

Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2022. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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