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Causal Inference and Discovery in Python : Unlock the Secrets of Modern Causal Machine Learning with DoWhy, EconML, PyTorch and More by Aleksander Molak, Ajit Jaokar is a nonfiction available to read on EtoBox.

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Demystify causal inference and casual discovery by uncovering causal principles and merging them with powerful machine learning algorithms for observational and experimental data Purchase of the print or Kindle book includes a free PDF eBook Key Features Examine Pearlian causal concepts such as structural causal models, interventions, counterfactuals, and more Discover modern causal inference techniques for average and heterogenous treatment effect estimation Explore and leverage traditional and modern causal discovery methods Book Description 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

Who reads Causal Inference and Discovery in Python : Unlock the Secrets of Modern Causal Machine Learning with DoWhy, EconML, PyTorch and More?

Author

Aleksander Molak, Ajit Jaokar

Publisher

Packt Publishing, Limited

Published

2023

Language

EN

ISBN

9781804611739

Category

nonfiction

Subjects

Computer Science, Engineering, Science

Updated

2026-03-24