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Causal Inference and Discovery in Python. Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more

(ebook) (audiobook) (audiobook) Książka w języku angielskim
Causal Inference and Discovery in Python. Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more Aleksander Molak, Ajit Jaokar - okladka książki

Causal Inference and Discovery in Python. Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more Aleksander Molak, Ajit Jaokar - okladka książki

Causal Inference and Discovery in Python. Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more Aleksander Molak, Ajit Jaokar - audiobook MP3

Causal Inference and Discovery in Python. Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more Aleksander Molak, Ajit Jaokar - audiobook CD

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456
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Causal methods present unique challenges compared to traditional machine learning and statistics. Learning causality can be challenging, but it offers distinct advantages that elude a purely statistical mindset. Causal Inference and Discovery in Python helps you unlock the potential of causality.
You’ll start with basic motivations behind causal thinking and a comprehensive introduction to Pearlian causal concepts, such as structural causal models, interventions, counterfactuals, and more. Each concept is accompanied by a theoretical explanation and a set of practical exercises with Python code. Next, you’ll dive into the world of causal effect estimation, consistently progressing towards modern machine learning methods. Step-by-step, you’ll discover Python causal ecosystem and harness the power of cutting-edge algorithms. You’ll further explore the mechanics of how “causes leave traces” and compare the main families of causal discovery algorithms. The final chapter gives you a broad outlook into the future of causal AI where we examine challenges and opportunities and provide you with a comprehensive list of resources to learn more.
By the end of this book, you will be able to build your own models for causal inference and discovery using statistical and machine learning techniques as well as perform basic project assessment.

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O autorach książki

Aleksander Molak jest niezależnym badaczem i konsultantem w dziedzinie uczenia maszynowego. Współpracował z licznymi firmami w Europie, USA i Izraelu, gdzie uczestniczył w tworzeniu wielkoskalowych systemów uczenia maszynowego. Jest też współzałożycielem firmy Lespire.io, dostawcy szkoleń z zakresu sztucznej inteligencji dla zespołów korporacyjnych. 

Ajit Jaokar is a data scientist for Feynlabs, building AI prototypes for complex applications. He is also a course director for artificial intelligence at the University of Oxford. Besides this, Ajit is a visiting fellow in Engineering Sciences at the University of Oxford and conducts AI courses at the London School of Economics, Universidad Politécnica de Madrid, and the Harvard Kennedy School of Government as part of The Future Society.
His work at Oxford and his company is based on interdisciplinary aspects of artificial intelligence, including AI with digital twins, quantum computing, metaverse, Agtech, and life sciences. His teaching is based on a methodology for AI and cyber-physical systems, which he is developing as part of his research.

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