Artificial Intelligence and Causal Inference  book cover
1st Edition

Artificial Intelligence and Causal Inference

ISBN 9780367859404
Published March 8, 2022 by Chapman and Hall/CRC
394 Pages 72 B/W Illustrations

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Book Description

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 mechanism underlying intelligence, including reasoning, planning and imagination. Understanding, transfer and generalization are major principles that give rise intelligence. One of a key component 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 the 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 the AI and modern causal analysis for further facilitating the AI revolution. This book is ideal for graduate students and researchers in AI, data science, causal inference, statistics, genomics, bioinformatics  and precision medicine. 

Key Features:

  • Cover three types of neural networks, formulate deep learning as an optimal control problem and use Pontryagin’s Maximum Principle for network training.
  • Deep learning for nonlinear mediation and instrumental variable causal analysis.
  • Construction of causal networks is formulated as a continuous optimization problem.
  • Transformer and attention are used to encode-decode graphics. RL is used to infer large causal networks.
  • Use VAE, GAN,  neural differential equations, recurrent neural network (RNN) and RL to estimate counterfactual outcomes.
  • AI-based methods for estimation of individualized treatment effect in the presence of network interference.

Table of Contents

Chapter 1 Deep Neural Networks

Chapter 2 Gaussian Processes and Learning  Dynamic for Wide Neural Networks

Chapter 3 Deep Generative Models

Chapter 4 Generative Adversarial Networks

Chapter 5 Deep Learning For Causal Inference

Chapter 6 Causal Inference in Time Series

Chapter 7 Deep Learning for Counterfactual Inference and Treatment Effect Estimation

Chapter 8 Reinforcement Learning and Causal 

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Momiao Xiong, is a professor in the Department of Biostatistics and Data Science, University of Texas School of Public Health, and a regular member in the Genetics & Epigenetics (G&E) Graduate Program at The University of Texas MD Anderson Cancer Center, UTHealth Graduate School of Biomedical Science. His interests are artificial intelligence, causal inference, bioinformatics and genomics.