Wang Y.-G., Liya Fu L., Paul S. Artificial Intelligence and Causal Inference

Boca Raton: CRC Press, 2022. — 253 p.Artificial Intelligence and Causal Inference address the recent development of relationships between artificial intelligence (AI) and causal inference. Despite significant progress in AI, a great challenge in AI development we are still facing is to understand mechanisms underlying intelligence, including reasoning, planning, and imagination. Understanding, transfer, and generalization are major principles that give rise to intelligence. One of the key components for understanding is causal inference. Causal inference includes intervention, domain shift learning, temporal structure, and counterfactual thinking as major concepts to understand causation and reasoning. Unfortunately, these essential components of the causality are often overlooked by machine learning, which leads to some failure of deep learning. AI and causal inference involve (1) using AI techniques as major tools for causal analysis and (2) applying the causal concepts and causal analysis methods to solving AI problems. The purpose of this book is to fill the gap between AI and modern causal analysis to further facilitate the AI revolution. This book is ideal for graduate students and researchers in AI, data science, causal inference, statistics, genomics, bioinformatics, and precision medicine.