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008			160210s2016 nju ob 001|0|eng|d
015			_aGBB624874 _2bnb
020			_z9781119186847 (pbk.) : _c$49.95
037			_a9781119186861 _bWiley
040			_aStDuBDS _beng _cIISERB _dUk _erda _epn
042			_aukblsr
082	0	4	_a519.54 P316C _223
100	1	0	_aPearl, Judea. _928984
245	1	0	_aCausal inference in statistics : _ba primer _cJudea Pearl, Madelyn Glymour, Nicholas Jewell.
250			_a1st
260			_aThe Atrium: _bJohn Wiley & Sons, _c2016.
300			_axvii, 136p.
504			_aIncludes bibliographical references and index.
505	0		_a<p>About the Authors ix</p> <p>Preface xi</p> <p>List of Figures xv</p> <p>About the Companion Website xix</p> <p><b>1 Preliminaries: Statistical and Causal Models 1</b></p> <p>1.1 Why Study Causation 1</p> <p>1.2 Simpson’s Paradox 1</p> <p>1.3 Probability and Statistics 7</p> <p>1.3.1 Variables 7</p> <p>1.3.2 Events 8</p> <p>1.3.3 Conditional Probability 8</p> <p>1.3.4 Independence 10</p> <p>1.3.5 Probability Distributions 11</p> <p>1.3.6 The Law of Total Probability 11</p> <p>1.3.7 Using Bayes’ Rule 13</p> <p>1.3.8 Expected Values 16</p> <p>1.3.9 Variance and Covariance 17</p> <p>1.3.10 Regression 20</p> <p>1.3.11 Multiple Regression 22</p> <p>1.4 Graphs 24</p> <p>1.5 Structural Causal Models 26</p> <p>1.5.1 Modeling Causal Assumptions 26</p> <p>1.5.2 Product Decomposition 29</p> <p><b>2 Graphical Models and Their Applications 35</b></p> <p>2.1 Connecting Models to Data 35</p> <p>2.2 Chains and Forks 35</p> <p>2.3 Colliders 40</p> <p>2.4 <i>d</i>-separation 45</p> <p>2.5 Model Testing and Causal Search 48</p> <p><b>3 The Effects of Interventions 53</b></p> <p>3.1 Interventions 53</p> <p>3.2 The Adjustment Formula 55</p> <p>3.2.1 To Adjust or not to Adjust? 58</p> <p>3.2.2 Multiple Interventions and the Truncated Product Rule 60</p> <p>3.3 The Backdoor Criterion 61</p> <p>3.4 The Front-Door Criterion 66</p> <p>3.5 Conditional Interventions and Covariate-Specific Effects 70</p> <p>3.6 Inverse Probability Weighing 72</p> <p>3.7 Mediation 75</p> <p>3.8 Causal Inference in Linear Systems 78</p> <p>3.8.1 Structural versus Regression Coefficients 80</p> <p>3.8.2 The Causal Interpretation of Structural Coefficients 81</p> <p>3.8.3 Identifying Structural Coefficients and Causal Effect 83</p> <p>3.8.4 Mediation in Linear Systems 87</p> <p><b>4 Counterfactuals and Their Applications 89</b></p> <p>4.1 Counterfactuals 89</p> <p>4.2 Defining and Computing Counterfactuals 91</p> <p>4.2.1 The Structural Interpretation of Counterfactuals 91</p> <p>4.2.2 The Fundamental Law of Counterfactuals 93</p> <p>4.2.3 From Population Data to Individual Behavior – An Illustration 94</p> <p>4.2.4 The Three Steps in Computing Counterfactuals 96</p> <p>4.3 Nondeterministic Counterfactuals 98</p> <p>4.3.1 Probabilities of Counterfactuals 98</p> <p>4.3.2 The Graphical Representation of Counterfactuals 101</p> <p>4.3.3 Counterfactuals in Experimental Settings 103</p> <p>4.3.4 Counterfactuals in Linear Models 106</p> <p>4.4 Practical Uses of Counterfactuals 107</p> <p>4.4.1 Recruitment to a Program 107</p> <p>4.4.2 Additive Interventions 109</p> <p>4.4.3 Personal Decision Making 111</p> <p>4.4.4 Sex Discrimination in Hiring 113</p> <p>4.4.5 Mediation and Path-disabling Interventions 114</p> <p>4.5 Mathematical Tool Kits for Attribution and Mediation 116</p> <p>4.5.1 A Tool Kit for Attribution and Probabilities of Causation 116</p> <p>4.5.2 A Tool Kit for Mediation 120</p> <p>References 127</p> <p>Index 133</p>
650		0	_aMathematical statistics. _928985
650		0	_aInference. _928986
650		0	_aCausation. _928987
650		0	_aProbabilities. _928988
700	1		_aGlymour, Madelyn, _eauthor. _928989
700	1	0	_aJewell, Nicholas P. _eauthor. _928990
776	0	8	_iPrint version : _z9781119186847
903			_aELD.DS.54139
942			_2ddc _cBK
999			_c9964 _d9964