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
Who this book is for
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.