Causal Inference and Discovery in Python

Causal Inference and Discovery in Python is a comprehensive guide to understanding and applying causal inference concepts and techniques. With practical examples and Python implementations, this book enables you to master causal modeling and leverage data for advanced decision-making in machine learning and data analysis.

What this Book will help me do

• Build a solid foundation in causal inference concepts, including structural causal models and their practical applications.
• Learn to implement causal estimation techniques for assessing treatment effects using Python.
• Understand and apply the four-step causal inference process through practical exercises and examples.
• Explore advanced methodologies like uplift modeling and deep learning applications in causal inference.
• Gain insights into causal discovery techniques and the future of causal artificial intelligence.

Author(s)

Aleksander Molak, the author of Causal Inference and Discovery in Python, is an experienced researcher and practitioner in the fields of machine learning and data science. He specializes in causal inference and machine learning applications, and in this book, he combines theoretical insights with practical Python tutorials to create an engaging and informative learning journey.

Who is it for?

This book is ideal for machine learning engineers, researchers, and data scientists looking to enhance their skills by incorporating causal inference into their work. It also suits individuals familiar with causal inference in other programming languages who wish to transition to Python, as well as beginners eager to dive into the world of causal AI and its applications.