{"product_id":"causal-inference-and-discovery-in-python-unlock-the-secrets-of-modern-causal-machine-learning-with-dowhy-econml-pytorch-and-more-9781804612989","title":"Causal Inference and Discovery in Python: Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more","description":"\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\u003eKey Features\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\u003eBook Description\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. 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.\u003c\/p\u003e\u003cp\u003eBy the end of this book, you will be able to build your own models for causal inference and discovery using statistical and machine learning techniques as well as perform basic project assessment.\u003c\/p\u003eWhat you will learn\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\u003eWho this book is for\u003cp\u003eThis book is for machine learning engineers, researchers, and data scientists looking to extend their toolkit and explore causal machine learning. It will also help people who've worked with causality using other programming languages and now want to switch to Python, those who worked with traditional causal inference and want to learn about causal machine learning, and tech-savvy entrepreneurs who want to go beyond the limitations of traditional ML. You are expected to have basic knowledge of Python and Python scientific libraries along with knowledge of basic probability and statistics.\u003c\/p\u003eTable of Contents\u003col\u003e\n\u003cli\u003eCausality - Hey, We Have Machine Learning, So Why Even Bother?\u003c\/li\u003e\n\u003cli\u003eJudea Pearl and the Ladder of Causation\u003c\/li\u003e\n\u003cli\u003eRegression, Observations, and Interventions\u003c\/li\u003e\n\u003cli\u003eGraphical Models\u003c\/li\u003e\n\u003cli\u003eForks, Chains, and Immoralities\u003c\/li\u003e\n\u003cli\u003eNodes, Edges, and Statistical (In)dependence\u003c\/li\u003e\n\u003cli\u003eThe Four-Step Process of Causal Inference\u003c\/li\u003e\n\u003cli\u003eCausal Models - Assumptions and Challenges\u003c\/li\u003e\n\u003cli\u003eCausal Inference and Machine Learning - from Matching to Meta- Learners\u003c\/li\u003e\n\u003cli\u003eCausal Inference and Machine Learning - Advanced Estimators, Experiments, Evaluations, and More\u003c\/li\u003e\n\u003cli\u003eCausal Inference and Machine Learning - Deep Learning, NLP, and Beyond\u003c\/li\u003e\n\u003cli\u003eCan I Have a Causal Graph, Please?\u003c\/li\u003e\n\u003cli\u003eCausal Discovery and Machine Learning - from Assumptions to Applications\u003c\/li\u003e\n\u003cli\u003eCausal Discovery and Machine Learning - Advanced Deep Learning and Beyond\u003c\/li\u003e\n\u003cli\u003eEpilogue\u003c\/li\u003e\n\u003c\/ol\u003e\u003cbr\u003e","brand":"Packt Publishing Ltd.","offers":[{"title":"Default Title","offer_id":50613580792082,"sku":"9781804612989","price":49.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0831\/4771\/8930\/files\/img_072e401c-89db-4ea5-a3c9-306f0b3af48b.jpg?v=1732409642","url":"https:\/\/surprise-castle.myshopify.com\/products\/causal-inference-and-discovery-in-python-unlock-the-secrets-of-modern-causal-machine-learning-with-dowhy-econml-pytorch-and-more-9781804612989","provider":"Surprise Castle","version":"1.0","type":"link"}