Bridging cutting-edge theoretical foundations with high-impact, real-world applications across healthcare, cybersecurity, and autonomous systems, this essential guide empowers researchers, practitioners, and policymakers to move beyond brittle correlation models and lead the next wave of transparent, safe, and truly interpretable AI.
Causal Artificial Intelligence (CAI) represents a significant advancement in the development of AI systems, marking a shift from purely correlation-based models to those capable of understanding and reasoning cause-and-effect relationships. In the context of disciplinary and industry development, casual AI addresses critical challenges in AI's ability to generalize, explain decisions, and make safe interventions across dynamic environments. Its emergence is reshaping fields like healthcare, economics, autonomous systems, and social sciences, offering a more reliable and interpretable form of AI that can predict the effects of actions, simulate outcomes, and inform decision-making more accurately. This book provides a comprehensive exploration of how causal reasoning can enhance the transparency, reliability, and ethical foundations of artificial intelligence systems. Addressing both theoretical and practical dimensions, it examines structural causal models, explainable AI, and privacy-preserving learning frameworks across diverse fields, including healthcare, climate science, manufacturing, automotive safety, and cybersecurity. It highlights innovative research in areas such as CNN-based causal inference for signal detection, explainable mobile AI for disease prediction, image processing for Ayurvedic material identification, and AI-driven systems for driver fatigue and collision detection. By bridging theoretical foundations with real-world applications, this book serves as a valuable resource for researchers, practitioners, and policymakers committed to developing transparent, secure, reliable, and ethically aligned intelligent technologies.
Readers will find the volume:
• Offers a deep dive into frameworks like structural causal models (SCMs), Bayesian networks, and do-calculus, allowing readers to explore how AI systems can identify and leverage cause-and-effect relationships;
• Emphasizes real-world applications of causal artificial intelligence through detailed case studies in fields such as healthcare, economics, and autonomous systems;
• Illustrates how causal models are applied in complex, dynamic environments, demonstrating casual AI's potential to revolutionize industries by providing actionable insights;
• Focuses on the ethical implications and interpretability of AI systems built on causal reasoning.
Audience
AI researchers, data scientists, academics, industry professionals, business leaders, medical researchers, and policymakers interested in advancing from traditional machine learning techniques to causal models.
