Causal Inference and Discovery in Python
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.
Offered by
Difficulty Level
Intermediate
Completion Time
15h12m
Language
English
About Book
Who Is This Book 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.
Causal Inference and Discovery in Python
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 15h12m total length
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
Related Resources
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