{"product_id":"causal-inference-and-discovery-in-python-unlock-the-secrets-of-modern-causal-machine-learning-with-dowhy-econml-pytorch-and-more-paperback","title":"Causal Inference and Discovery in Python: Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eAleksander Molak\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eDemystify causal inference and casual discovery by uncovering causal principles and merging them with powerful machine learning algorithms for observational and experimental data\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003ePurchase of the print or Kindle book includes a free PDF eBook\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eKey Features: \u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eExamine Pearlian causal concepts such as structural causal models, interventions, counterfactuals, and more\u003c\/li\u003e\n\u003cli\u003eDiscover modern causal inference techniques for average and heterogenous treatment effect estimation\u003c\/li\u003e\n\u003cli\u003eExplore and leverage traditional and modern causal discovery methods\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eBook Description: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eCausal 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.\u003c\/p\u003e\u003cp\u003eYou'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.\u003c\/p\u003e\u003cp\u003eNext, 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.\u003c\/p\u003e\u003cp\u003eThe 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.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWhat You Will Learn: \u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eMaster the fundamental concepts of causal inference\u003c\/li\u003e\n\u003cli\u003eDecipher the mysteries of structural causal models\u003c\/li\u003e\n\u003cli\u003eUnleash the power of the 4-step causal inference process in Python\u003c\/li\u003e\n\u003cli\u003eExplore advanced uplift modeling techniques\u003c\/li\u003e\n\u003cli\u003eUnlock the secrets of modern causal discovery using Python\u003c\/li\u003e\n\u003cli\u003eUse causal inference for social impact and community benefit\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWho this book is for: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eThis 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.\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 456\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.92 x 9.25 x 7.5 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e May 31, 2023\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":52491814240563,"sku":"9781804612989","price":84.74,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0300\/5595\/6612\/files\/SmdNNm5ORWdzZVNkWUpyNVdnblBRdz09.webp?v=1759924708","url":"https:\/\/www.vysn.com\/products\/causal-inference-and-discovery-in-python-unlock-the-secrets-of-modern-causal-machine-learning-with-dowhy-econml-pytorch-and-more-paperback","provider":"VYSN","version":"1.0","type":"link"}