Causal inference and discovery in python : unlock the secrets of modern causal machine learning with dowhy, econML, pytorch and more
Molak, Aleksander
Causal inference and discovery in python : unlock the secrets of modern causal machine learning with dowhy, econML, pytorch and more by Aleksander Molak - England : Packt Publishing, ©2023 - xxv, 429 p. : ill. ; 26 cm.
Includes bibliographical references and index.
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
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.
9781804612989
Aprenentatge automàtic.
Machine learning.
Python (Computer program language)
Python (Llenguatge de programació)
006.31 / MOL-C
Causal inference and discovery in python : unlock the secrets of modern causal machine learning with dowhy, econML, pytorch and more by Aleksander Molak - England : Packt Publishing, ©2023 - xxv, 429 p. : ill. ; 26 cm.
Includes bibliographical references and index.
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
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.
9781804612989
Aprenentatge automàtic.
Machine learning.
Python (Computer program language)
Python (Llenguatge de programació)
006.31 / MOL-C