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.
publication date
2023-05-31
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
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
approach
Complete with step-by-step explanations of essential concepts, practical examples, and implementations of key algorithms and concepts. We begin by learning basic causality concepts and differences between purely statistical and causal approaches to data analysis. After building solid foundations in causality, we move to learn to model causal relationships using basic and advanced techniques.
audience
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.
short description
Causal Inference and Discovery in Python is a comprehensive exploration of the theory and techniques at the intersection of modern causality and machine learning. It covers fundamental concepts of Pearlian causal inference, explains the theory, and provides step-by-step code examples for both traditional and advanced causal inference and discovery techniques.
subtitle
Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more
keywords
Machine learning python, Causality judea pearl, Causal inference python, Causal inference in python, Causal inference judea pearl
Product ISBN
9781804612989
