{"product_id":"artificial-intelligence-and-causal-inference-paperback","title":"Artificial Intelligence and Causal Inference - Paperback","description":"\u003cp\u003eby \u003cb\u003eMomiao Xiong\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eArtificial Intelligence and Causal Inference \u003c\/b\u003eaddress the recent development of relationships between artificial intelligence\u003cb\u003e (AI) \u003c\/b\u003eand 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. \u003c\/p\u003e\u003cp\u003eKey Features: \u003c\/p\u003e\u003cul\u003e \u003cli\u003eCover three types of neural networks, formulate deep learning as an optimal control problem and use Pontryagin's Maximum Principle for network training.\u003c\/li\u003e \u003cli\u003eDeep learning for nonlinear mediation and instrumental variable causal analysis.\u003c\/li\u003e \u003cli\u003eConstruction of causal networks is formulated as a continuous optimization problem.\u003c\/li\u003e \u003cli\u003eTransformer and attention are used to encode-decode graphics. RL is used to infer large causal networks.\u003c\/li\u003e \u003cli\u003eUse VAE, GAN, neural differential equations, recurrent neural network (RNN) and RL to estimate counterfactual outcomes.\u003c\/li\u003e \u003cli\u003eAI-based methods for estimation of individualized treatment effect in the presence of network interference.\u003c\/li\u003e \u003c\/ul\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eMomiao Xiong, \u003c\/b\u003eis a professor in the Department of Biostatistics and Data Science, University of Texas School of Public Health, and a regular member in the Genetics \u0026amp; Epigenetics (G\u0026amp;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.\u003c\/p\u003e\u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 368\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.81 x 11 x 8.25 IN\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eIllustrated:\u003c\/strong\u003e Yes\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e May 27, 2024\u003c\/div\u003e","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":42726060392511,"sku":"9781032193281","price":124.39,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0105\/8226\/1823\/files\/4205f1d0bf97b1511fe3c994d8c2fc59.webp?v=1765109073","url":"https:\/\/dhlswag.com\/products\/artificial-intelligence-and-causal-inference-paperback","provider":"BBB","version":"1.0","type":"link"}