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Causal Inference in Statistics - A Primer
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Causal Inference in Statistics - A Primer
von: Judea Pearl, Madelyn Glymour, Nicholas P. Jewell
Wiley, 2016
ISBN: 9781119186854
159 Seiten, Download: 2713 KB
 
Format:  PDF
geeignet für: Apple iPad, Android Tablet PC's Online-Lesen PC, MAC, Laptop

Typ: A (einfacher Zugriff)

 

 
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Inhaltsverzeichnis

  Cover 1  
  Title Page 5  
  Copyright 6  
  Dedication 7  
  Contents 9  
  About the Authors 11  
  Preface 13  
  List of Figures 17  
  About the Companion Website 21  
  Chapter 1 Preliminaries: Statistical and Causal Models 23  
     1.1 Why Study Causation 23  
     1.2 Simpson's Paradox 23  
     1.3 Probability and Statistics 29  
        1.3.1 Variables 29  
        1.3.2 Events 30  
        1.3.3 Conditional Probability 30  
        1.3.4 Independence 32  
        1.3.5 Probability Distributions 33  
        1.3.6 The Law of Total Probability 33  
        1.3.7 Using Bayes' Rule 35  
        1.3.8 Expected Values 38  
        1.3.9 Variance and Covariance 39  
        1.3.10 Regression 42  
        1.3.11 Multiple Regression 44  
     1.4 Graphs 46  
     1.5 Structural Causal Models 48  
        1.5.1 Modeling Causal Assumptions 48  
        1.5.2 Product Decomposition 51  
  Chapter 2 Graphical Models and Their Applications 57  
     2.1 Connecting Models to Data 57  
     2.2 Chains and Forks 57  
     2.3 Colliders 62  
     2.4 d-separation 67  
     2.5 Model Testing and Causal Search 70  
  Chapter 3 The Effects of Interventions 75  
     3.1 Interventions 75  
     3.2 The Adjustment Formula 77  
        3.2.1 To Adjust or not to Adjust? 80  
        3.2.2 Multiple Interventions and the Truncated Product Rule 82  
     3.3 The Backdoor Criterion 83  
     3.4 The Front-Door Criterion 88  
     3.5 Conditional Interventions and Covariate-Specific Effects 92  
     3.6 Inverse Probability Weighing 94  
     3.7 Mediation 97  
     3.8 Causal Inference in Linear Systems 100  
        3.8.1 Structural versus Regression Coefficients 102  
        3.8.2 The Causal Interpretation of Structural Coefficients 103  
        3.8.3 Identifying Structural Coefficients and Causal Effect 105  
        3.8.4 Mediation in Linear Systems 109  
  Chapter 4 Counterfactuals and Their Applications 111  
     4.1 Counterfactuals 111  
     4.2 Defining and Computing Counterfactuals 113  
        4.2.1 The Structural Interpretation of Counterfactuals 113  
        4.2.2 The Fundamental Law of Counterfactuals 115  
        4.2.3 From Population Data to Individual Behavior-An Illustration 116  
        4.2.4 The Three Steps in Computing Counterfactuals 118  
     4.3 Nondeterministic Counterfactuals 120  
        4.3.1 Probabilities of Counterfactuals 120  
        4.3.2 The Graphical Representation of Counterfactuals 123  
        4.3.3 Counterfactuals in Experimental Settings 125  
        4.3.4 Counterfactuals in Linear Models 128  
     4.4 Practical Uses of Counterfactuals 129  
        4.4.1 Recruitment to a Program 129  
        4.4.2 Additive Interventions 131  
        4.4.3 Personal Decision Making 133  
        4.4.4 Sex Discrimination in Hiring 135  
        4.4.5 Mediation and Path-disabling Interventions 136  
     4.5 Mathematical Tool Kits for Attribution and Mediation 138  
        4.5.1 A Tool Kit for Attribution and Probabilities of Causation 138  
        4.5.2 A Tool Kit for Mediation 142  
  References 149  
  Index 155  
  EULA 159  


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