Causal Artificial Intelligence - The Next Step in Effective Business Ai

Discover the next major revolution in data science and AI and how it applies to your organization
 
In Causal Artificial Intelligence: The Next Step in Effective, Efficient, and Practical AI, a team of dedicated tech executives delivers a business-focused approach based on a deep and engaging exploration of the models and data used in causal AI. The book's discussions include both accessible and understandable technical detail and business context and concepts that frame causal AI in familiar business settings.
 
Useful for both data scientists and business-side professionals, the book offers:
* Clear and compelling descriptions of the concept of causality and how it can benefit your organization
* Detailed use cases and examples that vividly demonstrate the value of causality for solving business problems
* Useful strategies for deciding when to use correlation-based approaches and when to use causal inference
 
An enlightening and easy-to-understand treatment of an essential business topic, Causal Artificial Intelligence is a must-read for data scientists, subject matter experts, and business leaders seeking to familiarize themselves with a rapidly growing area of AI application and research.

List of contents

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

About the author

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.

Summary

Discover the next major revolution in data science and AI and how it applies to your organization

In Causal Artificial Intelligence: The Next Step in Effective, Efficient, and Practical AI, a team of dedicated tech executives delivers a business-focused approach based on a deep and engaging exploration of the models and data used in causal AI. The book's discussions include both accessible and understandable technical detail and business context and concepts that frame causal AI in familiar business settings.

Useful for both data scientists and business-side professionals, the book offers:
* Clear and compelling descriptions of the concept of causality and how it can benefit your organization
* Detailed use cases and examples that vividly demonstrate the value of causality for solving business problems
* Useful strategies for deciding when to use correlation-based approaches and when to use causal inference

An enlightening and easy-to-understand treatment of an essential business topic, Causal Artificial Intelligence is a must-read for data scientists, subject matter experts, and business leaders seeking to familiarize themselves with a rapidly growing area of AI application and research.

Product details

• Authors: HURWITZ, Judith S Hurwitz, Judith S. Hurwitz, Judith S. Thompson Hurwitz, John K Thompson, John K. Thompson, John K. Hurwitz Thompson
• Publisher: Wiley, John and Sons Ltd
• Languages: English
• Product format: Paperback / Softback
• Released: 01.09.2023
• EAN: 9781394184132
• ISBN: 978-1-394-18413-2
• No. of pages: 384
• Subjects: Natural sciences, medicine, IT, technology > IT, data processing > IT; Informatik, Künstliche Intelligenz, KI, Artificial Intelligence, AI, computer science