000			02029nam a22002417a 4500
005			20240210164507.0
008			240210b |||||||| |||| 00| 0 eng d
020			_a9781804612989
082			_a005.133 _bMOL
100			_aMolak, Aleksander _914220
245			_aCausal inference and discovery in Python: _bunlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more
260			_bPackt Publishing Ltd. _aBirmingham _c2023
300			_axxv, 423 p.
365			_aINR _b3699.00
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. (https://www.packtpub.com/product/causal-inference-and-discovery-in-python/9781804612989)
650			_aComputer science _913730
650			_aComputer programming _915627
650			_aProgramming languages _915628
650			_aPython _915629
650			_aMachine Learning _915068
700			_aJaokar, Ajit _915630
942			_cBK _2ddc
999			_c5960 _d5960