Casual Artificial Intelligent

,Contents
1. Cover
2. Table of Contents
3. Title Page
4. Foreword
5. Preface
6. Introduction
7. CHAPTER 1: Setting the Stage for Causal AI
1. Why Causality Is a Game Changer
2. Causal AI in Perspective with Analytics
3. Analytical Sophistication Model
4. Scope of Services to Support Causal AI
5. The Value of the Hybrid Team
6. The Promise of AI
7. Understanding the Core Concepts of Causal AI
8. Summary
9. Note
8. CHAPTER 2: Understanding the Valueof Causal AI
1. Defining Causal AI
2. The Origins of Causal AI
3. Why Causal AI Is the Next Generation of AI
4. The Business Imperative of a Causal Model
5. The Importance of Augmented Intelligence
6. The Importance of Data, Visualization, and Frameworks
7. Getting Started with Causal AI
8. Summary
9. Notes
9. CHAPTER 3: Elements of Causal AI
1. Conceptual Models
2. Process Models
3. Collaboration Between Business and Analytics Professionals
4. The Fundamental Building Blocks of Causal AI Models
5. The Elements of Visual Modeling
6. Summary
7. Notes
10. CHAPTER 4: Creating Practical Causal AI Models and Systems
1. Understanding Complex Models
2. Causal Modeling Process: Part 1
3. Causal-Based Approach: Part 2
4. Summary
5. Notes
11. CHAPTER 5: Creating a Model with a Hybrid Team
1. The Hybrid Team
2. Defining Roles

, 3. The Basics Steps for a Hybrid Team Project
4. An Overview of Model Creation
5. Summary
12. CHAPTER 6: Explainability, Bias Detection, and AI Responsibility in Causal AI
1. Explainability
2. Detecting Bias and Ensuring Responsible AI
3. Summary
4. Note
13. CHAPTER 7: Tools, Practices, and Techniques to Enable Causal AI
1. The Causal AI Pipeline
2. The Importance of Synthetic Data
3. Current State of Tools and Software in Causal AI
4. Summary
14. CHAPTER 8: Causal AI in Action
1. Enterprise Marketing in a Business-to-Consumer Scenario
2. Moving from Strategy to Building and Implementing Causal AI Solutions
3. Summary
15. CHAPTER 9: The Future of Causal AI
1. Where We Stand Today
2. Foundations of Causal AI
3. The Causal AI Journey
4. Integrating Causal AI and Traditional AI
5. The Imperative for Managing Data
6. Ensembles of Data
7. Generative AI Is Emerging as a Game Changer for Causal AI
8. The Future of Causal Discovery
9. The Emergence of Causal AI Reinforcement Learning Will Accelerate Model
Training
10. Causal AI as a Common Language Between Business Leaders and Data Scientists
11. The Emergence of Probabilistic Programming Languages
12. The Predictable Model Evolution Cycle
13. The Emergence of the Digital Twin
14. Improving the Ability to Understand Ground Truth
15. The Development of More Sophisticated DAGs
16. Visualizing Complex Relationships in the DAGs
17. The Merging of Causal and Traditional AI Models
18. The Future of Explainability
19. The Evolution of Responsible AI
20. Advances in Data Security and Privacy
21. Integration Will Be Between Models and Business Applications
22. Summary
16. Glossary
17. Appendix: Causal AI Tools and Libraries
18. Selected Resources
19. Acknowledgments
20. About the Authors

, 21. About the Contributor
22. Index
23. Copyright
24. Dedication
25. End User License Agreement

List of Tables
1. Chapter 3
1. Table 3.1 Basic Entities in an Entry-Level Causal Model
2. Chapter 4
1. Table 4.1 Definition of Common Effects in a Causal Model
2. Table 4.2 Refined and Extended Effects in a Causal Model
3. Chapter 5
1. Table 5.1 Key Roles in the Causal AI Team

List of Illustrations
1. Chapter 1
1. FIGURE 1.1 Analytical sophistication model
2. FIGURE 1.2 The collaborative process begins by articulating the problem bein...
3. FIGURE 1.3 Causal model demonstrating the relationships between Product Qual...
2. Chapter 2
1. FIGURE 2.1 A replica of the Broad Street water pump.
2. FIGURE 2.2 The Ladder of Causation indicates the stages of Pearl's view of c...
3. FIGURE 2.3 An example of a simple causal model of a marketing campaign.
3. Chapter 3
1. FIGURE 3.1 The core correlation-based AI model begins by identifying raw dat...
2. FIGURE 3.2 Causal-based AI model, part 1. A causal AI model models the proce...
3. FIGURE 3.3 A sample DAG describes the basic relationships in a model.
4. FIGURE 3.4 In Wright's 1921 paper, “Correlation and Causation,” he used caus...
5. FIGURE 3.5 A sample DAG illustrating the relationship between stress and hea...
6. FIGURE 3.6 A DAG with weights illustrated
4. Chapter 4
1. FIGURE 4.1 The process of creating a causal AI model is an iterative process...
2. FIGURE 4.2 The DAG modified to include price as a treatment
3. FIGURE 4.3 The DAG modified to include a confounding variable
4. FIGURE 4.4 A mediator variable can unlock the relationship between variables...
5. FIGURE 4.5 A DAG illustrating the chain path type
6. FIGURE 4.6 A DAG illustrating the fork path type
7. FIGURE 4.7 A DAG illustrating the inverted fork path type
8. FIGURE 4.8 A DAG with an unobserved variable
9. FIGURE 4.9 Causal-based AI model: part 2
5. Chapter 5
1. FIGURE 5.1 A basic causal model to determine the cause and effect of a marke...