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 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.

What You Will Learn:

• Master the fundamental concepts of causal inference
• Decipher the mysteries of structural causal models
• Unleash the power of the 4-step causal inference process in Python
• Explore advanced uplift modeling techniques
• Unlock the secrets of modern causal discovery using Python
• Use causal inference for social impact and community benefit

Who this book is for:

This book is for machine learning engineers, data scientists, and machine learning researchers looking to extend their data science toolkit and explore causal machine learning. It will also help developers familiar with causality who have worked in another technology and want to switch to Python, and data scientists with a history of working with traditional causality who want to learn causal machine learning. It's also a must-read for tech-savvy entrepreneurs looking to build a competitive edge for their products and go beyond the limitations of traditional machine learning.

Formato	Tapa suave
Número de Páginas	456
Lenguaje	Inglés
Editorial	Packt Publishing
Fecha de Publicación	2023-05-31
Dimensiones	9.25" x 7.5" x 0.92" pulgadas
Letra Grande	No
Con Ilustraciones	No

Molak, Aleksander

Aleksander Molak is an independent machine learning researcher and consultant. Aleksander gained experience working with Fortune 100, Fortune 500, and Inc. 5000 companies across Europe, the USA, and Israel, helping them to build and design large-scale machine learning systems. On a mission to democratize causality for businesses and machine learning practitioners, Aleksander is a prolific writer, creator, and international speaker. As a co-founder of Lespire.io, an innovative provider of AI and machine learning training for corporate teams, Aleksander is committed to empowering businesses to harness the full potential of cutting-edge technologies that allow them to stay ahead of the curve.This book has been co-authored by many people whose ideas, love, and support left a significant trace in my life. I am deeply grateful to each one of you.