How do you know what might have happened, had you done things differently?
Causal machine learning gives you the insight you need to make predictions and control outcomes based on causal relationships instead of pure correlation, so you can make precise and timely interventions.
In Causal Machine Learning
you will learn how to:
Causal Machine Learning
- Build causal reinforcement learning algorithms
- Implement causal inference with modern probabilistic machine tools such as PyTorch and Pyro
- Compare and contrast statistical and econometric methods for causal inference
- Set up algorithms for attribution, credit assignment, and explanation
- Convert domain expertise into explainable causal models
is a practical introduction to building AI models that can reason about causality. Author Robert Ness, a leading researcher in causal AI at Microsoft Research, brings his unique expertise to this cutting-edge guide. His clear, code-first approach explains essential details of causal machine learning that are hidden in academic papers. Everything you learn can be easily and effectively applied to industry challenges, from building explainable causal models to predicting counterfactual outcomes.
about the technology
Causal machine learning is a major milestone in machine learning, allowing AI models to make accurate predictions based on causes rather than just correlations. Causal techniques help you make models that are more robust, explainable, and fair, and have a wide range of applications, from improving recommendation engines to perfecting self-driving cars.
about the book
Causal Machine Learning
teaches you how to build machine learning and deep learning models that implement causal reasoning. Discover why leading AI engineers are so excited by causal reasoning, and develop a high-level understanding of this next major trend in AI. New techniques are demonstrated with example models for solving industry-relevant problems. You’ll learn about causality for recommendations; causal modeling of online conversions; and uplift, attribution, and churn modeling. Each technique is tested against a common set of problems, data, and Python libraries, so you can compare and contrast which will work best for you.
about the reader
For data scientists and machine learning engineers. A familiarity with probability and statistics will be helpful, but not essential, to begin this guide. Examples in Python.
about the author
is a leading researcher in causal AI at Microsoft Research. He is a contributor to open-source causal inference packages such as Python’s DoWhy and R’s bnlearn.