{"product_id":"causal-ai-paperback","title":"Causal AI - Paperback","description":"\u003cb\u003eBuild AI models that can reliably deliver causal inference.\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003eHow do you know what might have happened, had you done things differently? Causal AI gives you the insight you need to make predictions and control outcomes based on causal relationships instead of pure correlation, so you can make precise and timely interventions. \u003ci\u003eCausal AI\u003c\/i\u003e is a practical introduction to building AI models that can reason about causality. \u003cp\u003e\u003c\/p\u003eIn \u003ci\u003eCausal AI\u003c\/i\u003e you will learn how to: \u003cp\u003e\u003c\/p\u003e- Build causal reinforcement learning algorithms\u003cbr\u003e - Implement causal inference with modern probabilistic machine tools such as PyTorch and Pyro\u003cbr\u003e - Compare and contrast statistical and econometric methods for causal inference\u003cbr\u003e - Set up algorithms for attribution, credit assignment, and explanation\u003cbr\u003e - Convert domain expertise into explainable causal models \u003cp\u003e\u003c\/p\u003e Author \u003cb\u003eRobert Osazuwa Ness\u003c\/b\u003e, a leading researcher in causal AI at Microsoft Research, brings his unique expertise to this cutting-edge guide. His clear, code-first approach explains essential details of causal machine learning that are hidden in academic papers. Everything you learn can be easily and effectively applied to industry challenges, from building explainable causal models to predicting counterfactual outcomes. \u003cp\u003e\u003c\/p\u003e Foreword by \u003cb\u003eLindsay Edwards\u003c\/b\u003e. \u003cp\u003e\u003c\/p\u003ePurchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eAbout the technology\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e Traditional ML models can't answer causal questions like, \"Why did that happen?\" or, \"What factors should I change to get a particular outcome?\" This book blends advanced statistical methods, computational techniques, and new algorithms to create machine learning systems that automate the process of causal inference. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eAbout the book\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e \u003ci\u003eCausal AI\u003c\/i\u003e introduces the tools, techniques, and algorithms of causal reasoning for machine learning. This unique book masterfully blends Bayesian and probabilistic approaches to causal inference with practical hands-on examples in Python. Along the way, you'll learn to integrate causal assumptions into deep learning architectures, including reinforcement learning and large language models. You'll also use PyTorch, Pyro, and other ML libraries to scale up causal inference. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eWhat's inside\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e- End-to-end causal inference with DoWhy\u003cbr\u003e - Deep Bayesian causal generative AI models\u003cbr\u003e - A code-first tour of the do-calculus and Pearl's causal hierarchy\u003cbr\u003e - Code for fine-tuning causal large language models \u003cp\u003e\u003c\/p\u003e\u003cb\u003eAbout the reader\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e For data scientists and machine learning engineers. Examples in Python. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eAbout the author\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e \u003cb\u003eRobert Osazuwa Ness\u003c\/b\u003e is an AI researcher at Microsoft Research and professor at Northeastern University. He is a contributor to open-source causal inference packages such as Python's DoWhy and R's bnlearn. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eTable of Contents\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e Part 1\u003cbr\u003e 1 Why causal AI\u003cbr\u003e 2 A primer on probabilistic generative modeling\u003cbr\u003e Part 2\u003cbr\u003e 3 Building a causal graphical model\u003cbr\u003e 4 Testing the DAG with causal constraints\u003cbr\u003e 5 Connecting causality and deep learning\u003cbr\u003e Part 3\u003cbr\u003e 6 Structural causal models\u003cbr\u003e 7 Interventions and causal effects\u003cbr\u003e 8 Counterfactuals and parallel worlds\u003cbr\u003e 9 The general counterfactual inference algorithm\u003cbr\u003e 10 Identification and the causal hierarchy\u003cbr\u003e Part 4\u003cbr\u003e 11 Building a causal inference workflow\u003cbr\u003e 12 Causal decisions and reinforcement learning\u003cbr\u003e 13 Causality and large language models","brand":"Manning Publications","offers":[{"title":"Default Title","offer_id":46584032002204,"sku":"9781633439917","price":63.99,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0712\/7616\/7324\/files\/9781633439917.jpg?v=1774478586","url":"https:\/\/bronzeandbrass.store\/products\/causal-ai-paperback","provider":"Bronze \u0026 Brass","version":"1.0","type":"link"}