Causal Artificial Intelligence - John K Thompson

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Résumé :

JUDITH S. HURWITZ is the chief evangelist at Geminos Software, a causal AI platform company. For more than 35 years she has been a strategist, technology consultant to software providers, and a thought leader having authored 10 books in topics ranging from augmented intelligence, data analytics, and cloud computing.

JOHN K. THOMPSON is an international technology executive with over 37 years of experience in the fields of data, advanced analytics, and artificial intelligence (AI). John is responsible for the global AI function at EY. He has previously led the global Artificial Intelligence and Rapid Data Lab teams at CSL Behring and is the bestselling author of three books on data analytics....

Sommaire:

Foreword xix

Preface xxiii

Introduction xxix

Chapter 1 Setting the Stage for Causal AI 1

Why Causality Is a Game Changer 2

Causal AI in Perspective with Analytics 7

Analytical Sophistication Model 8

Analytics Enablers 10

Analytics 10

Advanced Analytics 11

Scope of Services to Support Causal AI 11

The Value of the Hybrid Team 13

The Promise of AI 14

Understanding the Core Concepts of Causal AI 15

Explainability and Bias Detection 15

Explainability 17

Detecting Bias in a Model 17

Directed Acyclic Graphs 18

Structural Causal Model 19

Observed and Unobserved Variables 20

Counterfactuals 21

Confounders 21

Colliders 22

Front- Door and Backdoor Paths 23

Correlation 24

Causal Libraries and Tools 25

Propensity Score 25

Augmented Intelligence and Causal AI 26

Summary 27

Note 27

Chapter 2 Understanding the Value of Causal AI 29

Defining Causal AI 30

The Origins of Causal AI 33

Why Causality? 34

Expressing Relationships 37

The Ladder of Causation 38

Rung 1: Association, or Passive Observation 40

Rung 2: Intervention, or Taking Action 40

Rung 3: Counterfactuals, or Imagining What If 42

Why Causal AI Is the Next Generation of AI 43

Deep Learning and Neural Networks 43

Neural Networks 44

Establishing Ground Truth 45

The Business Imperative of a Causal Model 46

The Importance of Augmented Intelligence 51

The Importance of Data, Visualization, and Frameworks 52

Getting the Appropriate Data 52

Applying Data and Model Visualization 55

Applying Frameworks After Creating a Model 56

Getting Started with Causal AI 57

Summary 58

Notes 59

Chapter 3 Elements of Causal AI 61

Conceptual Models 62

Correlation vs. Causal Models 63

Correlation- Based AI 63

Causal AI 63

Understanding the Relationship Between Correlation and Causality 64

Process Models 66

Correlation- Based AI Process Model 67

Causal- Based AI Process Model 69

Collaboration Between Business and Analytics Professionals 72

The Fundamental Building Blocks of Causal AI Models 75

The Relations Between DAGs and SCMs 76

Explaining DAGs 76

Causal Notation: The Language of DAGs 78

Operationalizing a DAG with an SCM 79

The Elements of Visual Modeling 81

Nodes 83

Variables 83

Endogenous and Exogenous Variables 83

Observed and Unobserved Variables 84

Paths/Relationships 84

Weights 86

Summary 88

Notes 89

Chapter 4 Creating Practical Causal AI Models and Systems 91

Understanding Complex Models 92

Causal Modeling Process: Part 1 94

Step 1: What Are the Intended Outcomes? 95

Step 2: What Are the Proposed Interventions? 97

Step 3: What Are the Confounding Factors? 99

Step 4: What Are the Factors Creating the Effects and Changes? 102

Common/Universal Effects in a Causal Model 102

Refined Effects in a Causal Model 103

Step 5: Creating a Directed Acyclic Graph 105

Step 6: Paths and Relationships 105

Types of Paths 106

Path Connecting an Unobserved Variable 107

Front- Door Paths 108

Backdoor Paths 108

Modeling for Simplicity to Understand Complexity 109

Step 7: Data Acquisition 110

Causal- Based Approach: Part 2 112

Step 8: Data Integration 113

Step 9: Model Modification 114

Step 10: Data Transformation 115

Step 11: Prep...