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Paperback or Softback. Condizione: New. Causal Inference with Bayesian Networks: Build Bayesian Networks and Causal Inference Models with R and Python. Book.
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Paperback. Condizione: new. Paperback. Learn Bayesian networks, graphical models, and causal inference for probabilistic reasoning, treatment effect estimation, and decision-making using observational data with hands-on examples in R and Python.Key FeaturesApply Bayesian networks for probabilistic and causal inference.Estimate c…ausal effects from observational data using machine learning.Build practical causal inference workflows in R and Python.Book DescriptionThis practical guide explores Bayesian networks, graphical models, and causal inference for probabilistic reasoning and treatment effect estimation using real-world data. Youll learn Bayesian networks, conditional independence, structural causal models (SCM), and intervention-based reasoning for causal analysis. The book explains how graphical models support probabilistic inference, decision-making, and knowledge representation across healthcare, economics, epidemiology, finance, and social sciences.Youll work with probabilistic inference methods such as variable elimination, tree clustering, and Bayesian network reasoning. For causal inference, the book covers Pearls do-calculus, backdoor and front-door criteria, causal effect identification, and treatment effect estimation using observational data. Youll also explore the potential outcomes framework and machine learning approaches for causal inference, including meta-learners for estimating conditional average treatment effects and heterogeneous treatment effects.Practical examples and exercises in R and Python help reinforce concepts and build implementation skills for causal modeling workflows. By the end of the book, youll be able to design Bayesian network models, perform probabilistic and causal inference, and develop practical causal analysis applications for evidence-based decision-making.What you will learnBuild Bayesian networks for knowledge representationInterpret conditional independence in graphical modelsApply causal reasoning with structural causal modelsPerform probabilistic inference with Bayesian networksIdentify and estimate causal treatment effectsUse machine learning methods for causal inferenceImplement probabilistic and causal models in R and PythonWho this book is forThis book will serve as a valuable resource for a wide range of professionals including data scientists, software engineers, policy analysts, decision-makers, information technology professionals involved in developing expert systems or knowledge-based applications that deal with uncertainty, as well as researchers across diverse disciplines seeking insights into causal analysis and estimating treatment effects in randomized studies. The book will enable readers to leverage libraries in R and Python and build software prototypes for their own applications. Explore Bayesian networks, graphical models, and causal inference for probabilistic reasoning and treatment effect estimation. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Paperback. Condizione: new. Paperback. Learn Bayesian networks, graphical models, and causal inference for probabilistic reasoning, treatment effect estimation, and decision-making using observational data with hands-on examples in R and Python.Key FeaturesApply Bayesian networks for probabilistic and causal inference.Estimate c…ausal effects from observational data using machine learning.Build practical causal inference workflows in R and Python.Book DescriptionThis practical guide explores Bayesian networks, graphical models, and causal inference for probabilistic reasoning and treatment effect estimation using real-world data. Youll learn Bayesian networks, conditional independence, structural causal models (SCM), and intervention-based reasoning for causal analysis. The book explains how graphical models support probabilistic inference, decision-making, and knowledge representation across healthcare, economics, epidemiology, finance, and social sciences.Youll work with probabilistic inference methods such as variable elimination, tree clustering, and Bayesian network reasoning. For causal inference, the book covers Pearls do-calculus, backdoor and front-door criteria, causal effect identification, and treatment effect estimation using observational data. Youll also explore the potential outcomes framework and machine learning approaches for causal inference, including meta-learners for estimating conditional average treatment effects and heterogeneous treatment effects.Practical examples and exercises in R and Python help reinforce concepts and build implementation skills for causal modeling workflows. By the end of the book, youll be able to design Bayesian network models, perform probabilistic and causal inference, and develop practical causal analysis applications for evidence-based decision-making.What you will learnBuild Bayesian networks for knowledge representationInterpret conditional independence in graphical modelsApply causal reasoning with structural causal modelsPerform probabilistic inference with Bayesian networksIdentify and estimate causal treatment effectsUse machine learning methods for causal inferenceImplement probabilistic and causal models in R and PythonWho this book is forThis book will serve as a valuable resource for a wide range of professionals including data scientists, software engineers, policy analysts, decision-makers, information technology professionals involved in developing expert systems or knowledge-based applications that deal with uncertainty, as well as researchers across diverse disciplines seeking insights into causal analysis and estimating treatment effects in randomized studies. The book will enable readers to leverage libraries in R and Python and build software prototypes for their own applications. Explore Bayesian networks, graphical models, and causal inference for probabilistic reasoning and treatment effect estimation. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Paperback. Condizione: new. Paperback. Learn Bayesian networks, graphical models, and causal inference for probabilistic reasoning, treatment effect estimation, and decision-making using observational data with hands-on examples in R and Python.Key FeaturesApply Bayesian networks for probabilistic and causal inference.Estimate c…ausal effects from observational data using machine learning.Build practical causal inference workflows in R and Python.Book DescriptionThis practical guide explores Bayesian networks, graphical models, and causal inference for probabilistic reasoning and treatment effect estimation using real-world data. Youll learn Bayesian networks, conditional independence, structural causal models (SCM), and intervention-based reasoning for causal analysis. The book explains how graphical models support probabilistic inference, decision-making, and knowledge representation across healthcare, economics, epidemiology, finance, and social sciences.Youll work with probabilistic inference methods such as variable elimination, tree clustering, and Bayesian network reasoning. For causal inference, the book covers Pearls do-calculus, backdoor and front-door criteria, causal effect identification, and treatment effect estimation using observational data. Youll also explore the potential outcomes framework and machine learning approaches for causal inference, including meta-learners for estimating conditional average treatment effects and heterogeneous treatment effects.Practical examples and exercises in R and Python help reinforce concepts and build implementation skills for causal modeling workflows. By the end of the book, youll be able to design Bayesian network models, perform probabilistic and causal inference, and develop practical causal analysis applications for evidence-based decision-making.What you will learnBuild Bayesian networks for knowledge representationInterpret conditional independence in graphical modelsApply causal reasoning with structural causal modelsPerform probabilistic inference with Bayesian networksIdentify and estimate causal treatment effectsUse machine learning methods for causal inferenceImplement probabilistic and causal models in R and PythonWho this book is forThis book will serve as a valuable resource for a wide range of professionals including data scientists, software engineers, policy analysts, decision-makers, information technology professionals involved in developing expert systems or knowledge-based applications that deal with uncertainty, as well as researchers across diverse disciplines seeking insights into causal analysis and estimating treatment effects in randomized studies. The book will enable readers to leverage libraries in R and Python and build software prototypes for their own applications. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Taschenbuch. Condizione: Neu. Causal Inference with Bayesian Networks | Build Bayesian Networks and Causal Inference Models with R and Python | Yousri El Fattah (u. a.) | Taschenbuch | Englisch | 2026 | Packt Publishing | EAN 9781835084984 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[a…t]libri[dot]de | Anbieter: preigu Print on Demand.
