02755nam a22002297a 4500003000600000005001700006008004100023020001800064040001000082082001800092100002200110245015900132260004100291300003400332504005100366505081600417520115701233650003102390650002202421650004002443650004202483IIITD20240920020004.0240731b |||||||| |||| 00| 0 eng d a9781804612989 aIIITD a006.31bMOL-C aMolak, Aleksander aCausal inference and discovery in python :bunlock the secrets of modern causal machine learning with dowhy, econML, pytorch and morecby Aleksander Molak aEngland :bPackt Publishing,c©2023 axxv, 429 p. :bill. ;c26 cm. aIncludes bibliographical references and index. t1. Causality Hey, We Have Machine Learning, So Why Even Bother?t2. Judea Pearl and the Ladder of Causationt3. Regression, Observations, and Interventionst4. Graphical Modelst5. Forks, Chains, and Immoralitiest6. Nodes, Edges, and Statistical (In)dependencet7. The Four-Step Process of Causal Inferencet8. Causal Models Assumptions and Challengest9. Causal Inference and Machine Learning from Matching to Meta-Learnerst10. Causal Inference and Machine Learning Advanced Estimators, Experiments, Evaluations, and Moret11. Causal Inference and Machine Learning - Deep Learning, NLP, and Beyondt12. Can I Have a Causal Graph, Please?t13. Causal Discovery and Machine Learning - from Assumptions to Applicationst14. Causal Discovery and Machine Learning - Advanced Deep Learning and Beyondt15. Epilogue aCausal 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. aAprenentatge automàtic. aMachine learning. aPython (Computer program language) aPython (Llenguatge de programació)