Pearl, Judea.
Causal Inference in Statistics : A Primer. - 1 online resource (182 pages) - New York Academy of Sciences Ser. . - New York Academy of Sciences Ser. .
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.
9781119186861
Electronic books.
Causal Inference in Statistics : A Primer. - 1 online resource (182 pages) - New York Academy of Sciences Ser. . - New York Academy of Sciences Ser. .
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.
9781119186861
Electronic books.