Causal Inference and Discovery in Python

Demystify causal inference and casual discovery by uncovering causal principles and merging them with powerful machine learning algorithms for observational and experimental data

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.

Table of Contents
Causality – Hey, We Have Machine Learning, So Why Even Bother?
Judea Pearl and the Ladder of Causation
Regression, Observations, and Interventions
Graphical Models
Forks, Chains, and Immoralities
Nodes, Edges, and Statistical (In)dependence
The Four-Step Process of Causal Inference
Causal Models – Assumptions and Challenges
Causal Inference and Machine Learning – from Matching to Meta-Learners
Causal Inference and Machine Learning – Advanced Estimators, Experiments, Evaluations, and More
Causal Inference and Machine Learning – Deep Learning, NLP, and Beyond
Can I Have a Causal Graph, Please?
Causal Discovery and Machine Learning – from Assumptions to Applications
Causal Discovery and Machine Learning – Advanced Deep Learning and Beyond
Epilogue