Causal AI 1st Edition by Robert Osazuwa Ness

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Author: Robert Osazuwa Ness
 which seeks to go beyond traditional machine learning by focusing on understanding and modeling the cause-and-effect relationships within data. The book explores how CAI techniques can improve decision-making by enabling systems to reason about the impact of actions and interventions, rather than simply identifying patterns in past data. Ness provides a comprehensive overview of causal inference, integrating concepts from statistics, machine learning, and domain knowledge, and demonstrates how these approaches can be applied in real-world scenarios across industries like healthcare, economics, and business. The book emphasizes practical applications, while also covering the theoretical foundations of causal modeling, making it a valuable resource for both researchers and practitioners in AI and data science.

Causal AI 1st Edition Table of contents:

Chapter 1: Introduction to Causal Inference

• What is Causal AI?
• The Need for Causal Reasoning in Artificial Intelligence
• Differences Between Correlation and Causation
• Historical Development of Causal Inference

Chapter 2: Theoretical Foundations of Causal Models

• Overview of Causal Models
• Directed Acyclic Graphs (DAGs) and Causal Diagrams
• The Rubin Causal Model (RCM)
• Structural Causal Models (SCMs)

Chapter 3: Causal Discovery

• Approaches to Causal Discovery
• Constraints-Based Methods
• Score-Based Methods
• Hybrid Methods and Algorithms
• Case Studies in Causal Discovery

Chapter 4: Causal Inference Techniques

• Estimating Causal Effects
• Randomized Controlled Trials (RCTs)
• Observational Studies and Challenges
• Instrumental Variables and Natural Experiments
• Counterfactuals and Potential Outcomes

Chapter 5: Causal Machine Learning

• Machine Learning vs. Causal Inference
• Causal Effect Estimation in ML
• Causal Forests and Uplift Modeling
• Applications of Machine Learning in Causal Inference
• Causal AI in Big Data Environments

Chapter 6: Interventions and Decision Making

• Interventional Causal Inference
• The Role of Interventions in Causal AI
• Policy and Decision-Making Models
• Counterfactual Reasoning for Decisions
• Real-World Applications of Interventions

Chapter 7: Ethics and Challenges in Causal AI

• Ethical Implications of Causal AI
• Bias, Fairness, and Transparency
• Challenges in Interpreting Causal Models
• Ensuring Causal Inference Robustness
• Privacy and Security Considerations

Chapter 8: Practical Applications of Causal AI

• Causal AI in Healthcare
• Causal Models in Economics and Policy
• Business and Marketing Applications
• Causal AI in Social Sciences
• Causal Inference in Predictive Analytics

Chapter 9: Future Directions of Causal AI

• Emerging Trends and Innovations
• The Intersection of Causal AI and Deep Learning
• Potential for Causal AI in Autonomous Systems
• Challenges and Opportunities in Scaling Causal AI
• Future Research Directions

Chapter 10: Conclusion

• Summary of Key Takeaways
• The Importance of Causal Reasoning in AI
• Final Thoughts on the Evolution of Causal AI

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