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• 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

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.

By the end of this book, you will be able to build your own models for causal inference and discovery using statistical and machine learning techniques as well as perform basic project assessment.

• 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

This book is for machine learning engineers, researchers, and data scientists looking to extend their toolkit and explore causal machine learning. It will also help people who've worked with causality using other programming languages and now want to switch to Python, those who worked with traditional causal inference and want to learn about causal machine learning, and tech-savvy entrepreneurs who want to go beyond the limitations of traditional ML. You are expected to have basic knowledge of Python and Python scientific libraries along with knowledge of basic probability and statistics.

1. Causality - Hey, We Have Machine Learning, So Why Even Bother?
2. Judea Pearl and the Ladder of Causation
3. Regression, Observations, and Interventions
4. Graphical Models
5. Forks, Chains, and Immoralities
6. Nodes, Edges, and Statistical (In)dependence
7. The Four-Step Process of Causal Inference
8. Causal Models - Assumptions and Challenges
9. Causal Inference and Machine Learning - from Matching to Meta- Learners
10. Causal Inference and Machine Learning - Advanced Estimators, Experiments, Evaluations, and More
11. Causal Inference and Machine Learning - Deep Learning, NLP, and Beyond
12. Can I Have a Causal Graph, Please?
13. Causal Discovery and Machine Learning - from Assumptions to Applications
14. Causal Discovery and Machine Learning - Advanced Deep Learning and Beyond
15. Epilogue