000			02811nam a22002537a 4500
003			IIITD
005			20240920020004.0
008			240731b |||||||| |||| 00| 0 eng d
020			_a9781804612989
040			_aIIITD
082			_a006.31 _bMOL-C
100			_aMolak, Aleksander
245			_aCausal inference and discovery in python : _bunlock the secrets of modern causal machine learning with dowhy, econML, pytorch and more _cby Aleksander Molak
260			_aEngland : _bPackt Publishing, _c©2023
300			_axxv, 429 p. : _bill. ; _c26 cm.
504			_aIncludes bibliographical references and index.
505			_t1. Causality Hey, We Have Machine Learning, So Why Even Bother? _t2. Judea Pearl and the Ladder of Causation _t3. Regression, Observations, and Interventions _t4. Graphical Models _t5. Forks, Chains, and Immoralities _t6. Nodes, Edges, and Statistical (In)dependence _t7. The Four-Step Process of Causal Inference _t8. Causal Models Assumptions and Challenges _t9. Causal Inference and Machine Learning from Matching to Meta-Learners _t10. Causal Inference and Machine Learning Advanced Estimators, Experiments, Evaluations, and More _t11. Causal Inference and Machine Learning - Deep Learning, NLP, and Beyond _t12. Can I Have a Causal Graph, Please? _t13. Causal Discovery and Machine Learning - from Assumptions to Applications _t14. Causal Discovery and Machine Learning - Advanced Deep Learning and Beyond _t15. Epilogue
520			_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.
650			_aAprenentatge automàtic.
650			_aMachine learning.
650			_aPython (Computer program language)
650			_aPython (Llenguatge de programació)
942			_cBK _2ddc _02
999			_c189539 _d189539