Aydede Yigit (38 results)
- Hardcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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- Hardcover
Seller: Books Puddle, New York, NY, U.S.A.Books Puddle
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- Hardcover
Seller: Majestic Books, Hounslow, , United KingdomMajestic Books
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- Hardcover
Seller: Biblios, frankfurt am main, HESSE, GermanyBiblios
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- Hardcover
Seller: Books From California, Simi Valley, CA, U.S.A.Books From California
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hardcover. Condition: Fine.
- Hardcover
Seller: PBShop.store US, Wood Dale, IL, U.S.A.PBShop.store US
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- Hardcover
Seller: PBShop.store UK, Fairford, GLOS, United KingdomPBShop.store UK
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£ 78.23
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- Hardcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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£ 89.50
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- Hardcover
Seller: Ria Christie Collections, Uxbridge, United KingdomRia Christie Collections
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£ 77.11
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- Hardcover
Seller: Chiron Media, Wallingford, , United KingdomChiron Media
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£ 75.04
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- Hardcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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- Hardcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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- Hardcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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£ 86.54
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- Hardcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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£ 102.71
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- Hardcover
Seller: Rarewaves.com USA, London, LONDO, United KingdomRarewaves.com USA
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£ 111.81
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Hardback. Condition: New. Machine Learning Toolbox for Social Scientists covers predictive methods with complementary statistical "tools" that make it mostly self-contained. The inferential statistics is the traditional framework for most data analytics courses in social science and business fields, especially in Economics and F…inance. The new organization that this book offers goes beyond standard machine learning code applications, providing intuitive backgrounds for new predictive methods that social science and business students can follow. The book also adds many other modern statistical tools complementary to predictive methods that cannot be easily found in "econometrics" textbooks: nonparametric methods, data exploration with predictive models, penalized regressions, model selection with sparsity, dimension reduction methods, nonparametric time-series predictions, graphical network analysis, algorithmic optimization methods, classification with imbalanced data, and many others. This book is targeted at students and researchers who have no advanced statistical background, but instead coming from the tradition of "inferential statistics". The modern statistical methods the book provides allows it to be effectively used in teaching in the social science and business fields.Key Features: The book is structured for those who have been trained in a traditional statistics curriculum. There is one long initial section that covers the differences in "estimation" and "prediction" for people trained for causal analysis. The book develops a background framework for Machine learning applications from Nonparametric methods. SVM and NN simple enough without too much detail. It's self-sufficient. Nonparametric time-series predictions are new and covered in a separate section. Additional sections are added: Penalized Regressions, Dimension Reduction Methods, and Graphical Methods have been increasing in their popularity in social sciences.
- Hardcover
Seller: THE SAINT BOOKSTORE, Southport, , United KingdomTHE SAINT BOOKSTORE
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£ 87.67
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- Hardcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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- Hardcover
Seller: California Books, Miami, FL, U.S.A.California Books
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£ 118.31
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- Hardcover
Seller: Majestic Books, Hounslow, , United KingdomMajestic Books
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£ 111.65
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- Hardcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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£ 100.34
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Condition: As New. Unread book in perfect condition.
- Hardcover
Seller: Speedyhen, Hertfordshire, United KingdomSpeedyhen
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£ 78.74
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Condition: NEW.
- Hardcover
- First Edition
Seller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, IrelandKennys Bookshop and Art Galleries Ltd.
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£ 116.28
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Condition: New. 2023. 1st Edition. hardcover. . . . . .
- Softcover
Seller: moluna, Greven, , Germanymoluna
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£ 85.77
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- Hardcover
Seller: Rarewaves USA, OSWEGO, IL, U.S.A.Rarewaves USA
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£ 139.26
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Hardback. Condition: New. Causal Inference and Machine Learning in Economics, Social, and Health Sciences bridges the gap between modern machine learning methods and the applied needs of economists, public health researchers, and social scientists. Designed with students and practitioners in mind, the book introduces machine lea…rning through the lens of causal inference, offering a rigorous yet accessible roadmap for using data to answer real-world policy questions.It combines econometric and machine learning methods such as penalized regressions, random forests, boosting, double machine learning, and the most up-to-date estimation methods for addressing selection on observables (e.g., matching, AIPW) and unobservables (e.g., instrumental variables, difference-in-differences, synthetic control). Readers learn how to estimate treatment effects, uncover heterogeneity, and work with high-dimensional data, while gaining clarity on assumptions, trade-offs, and limitations. The book also covers advanced and often underrepresented topics such as time series forecasting with machine learning methods, neural networks and deep learning, and core optimization algorithms like gradient descent. Each method is introduced with intuition, formal treatment, and applied examples from economics, health, labor, and development studies. It places special emphasis on transparency, identification, and interpretability.Beyond introducing models, it provides step-by-step guidance from raw data to estimation, showing not just what works, but how and why-both methodologically and computationally. Unlike many texts that rely on pre-built software or assume deep technical knowledge, this book builds from foundational concepts such as estimation, error decomposition, and bias-variance trade-offs, then progresses to advanced machine learning approaches. Simulation-based pedagogy helps readers visualize model behavior under known conditions, enabling researchers and students alike to see how statistical tools perform across diverse empirical settings.A distinctive feature of the book is its focus on when and how to use predictive versus causal models. Rather than treating them as separate tasks, it shows how each can inform the other. Practical insights, diagnostics, and examples guide readers in selecting appropriate tools based on research goals and data characteristics.With its clear style, practical code in R, and integrated approach to prediction and causality, this book is an essential resource for applied researchers, students, and anyone using data to inform policy and decision-making.KEY FEATURESIntegrates causal inference with the latest econometric and machine learning methods to address real-world policy questions in economics, health, and the social sciences.Offers clear, detailed explanations and intuitive guidance-even for foundational concepts often overlooked in other sources-to build theoretical understanding and link econometric principles to application.Designed for applied researche.
- Hardcover
Seller: Books Puddle, New York, NY, U.S.A.Books Puddle
Contact seller4-star sellerCondition: New
£ 143.40
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Condition: New.
- Hardcover
Seller: Biblios, frankfurt am main, HESSE, GermanyBiblios
Contact seller4-star sellerCondition: New
£ 139.27
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Condition: New.
- Hardcover
Seller: Kennys Bookstore, Olney, MD, U.S.A.Kennys Bookstore
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£ 143.41
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Condition: New. 2023. 1st Edition. hardcover. . . . . . Books ship from the US and Ireland.
- Hardcover
Seller: Revaluation Books, Exeter, , United KingdomRevaluation Books
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£ 144.64
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Hardcover. Condition: Brand New. 336 pages. 10.00x7.00x1.26 inches. In Stock.
- Hardcover
Seller: Rarewaves.com UK, London, United KingdomRarewaves.com UK
Contact seller5-star sellerCondition: New
£ 101.90
£ 65.00 shippingShips from United Kingdom to U.S.A.Quantity: 1 available
Hardback. Condition: New. Machine Learning Toolbox for Social Scientists covers predictive methods with complementary statistical "tools" that make it mostly self-contained. The inferential statistics is the traditional framework for most data analytics courses in social science and business fields, especially in Economics and F…inance. The new organization that this book offers goes beyond standard machine learning code applications, providing intuitive backgrounds for new predictive methods that social science and business students can follow. The book also adds many other modern statistical tools complementary to predictive methods that cannot be easily found in "econometrics" textbooks: nonparametric methods, data exploration with predictive models, penalized regressions, model selection with sparsity, dimension reduction methods, nonparametric time-series predictions, graphical network analysis, algorithmic optimization methods, classification with imbalanced data, and many others. This book is targeted at students and researchers who have no advanced statistical background, but instead coming from the tradition of "inferential statistics". The modern statistical methods the book provides allows it to be effectively used in teaching in the social science and business fields.Key Features: The book is structured for those who have been trained in a traditional statistics curriculum. There is one long initial section that covers the differences in "estimation" and "prediction" for people trained for causal analysis. The book develops a background framework for Machine learning applications from Nonparametric methods. SVM and NN simple enough without too much detail. It's self-sufficient. Nonparametric time-series predictions are new and covered in a separate section. Additional sections are added: Penalized Regressions, Dimension Reduction Methods, and Graphical Methods have been increasing in their popularity in social sciences.
- Hardcover
Seller: Rarewaves.com USA, London, LONDO, United KingdomRarewaves.com USA
Contact seller5-star sellerCondition: New
£ 179.05
Free ShippingShips from United Kingdom to U.S.A.Quantity: Over 20 available
Hardback. Condition: New. Causal Inference and Machine Learning in Economics, Social, and Health Sciences bridges the gap between modern machine learning methods and the applied needs of economists, public health researchers, and social scientists. Designed with students and practitioners in mind, the book introduces machine lea…rning through the lens of causal inference, offering a rigorous yet accessible roadmap for using data to answer real-world policy questions.It combines econometric and machine learning methods such as penalized regressions, random forests, boosting, double machine learning, and the most up-to-date estimation methods for addressing selection on observables (e.g., matching, AIPW) and unobservables (e.g., instrumental variables, difference-in-differences, synthetic control). Readers learn how to estimate treatment effects, uncover heterogeneity, and work with high-dimensional data, while gaining clarity on assumptions, trade-offs, and limitations. The book also covers advanced and often underrepresented topics such as time series forecasting with machine learning methods, neural networks and deep learning, and core optimization algorithms like gradient descent. Each method is introduced with intuition, formal treatment, and applied examples from economics, health, labor, and development studies. It places special emphasis on transparency, identification, and interpretability.Beyond introducing models, it provides step-by-step guidance from raw data to estimation, showing not just what works, but how and why-both methodologically and computationally. Unlike many texts that rely on pre-built software or assume deep technical knowledge, this book builds from foundational concepts such as estimation, error decomposition, and bias-variance trade-offs, then progresses to advanced machine learning approaches. Simulation-based pedagogy helps readers visualize model behavior under known conditions, enabling researchers and students alike to see how statistical tools perform across diverse empirical settings.A distinctive feature of the book is its focus on when and how to use predictive versus causal models. Rather than treating them as separate tasks, it shows how each can inform the other. Practical insights, diagnostics, and examples guide readers in selecting appropriate tools based on research goals and data characteristics.With its clear style, practical code in R, and integrated approach to prediction and causality, this book is an essential resource for applied researchers, students, and anyone using data to inform policy and decision-making.KEY FEATURESIntegrates causal inference with the latest econometric and machine learning methods to address real-world policy questions in economics, health, and the social sciences.Offers clear, detailed explanations and intuitive guidance-even for foundational concepts often overlooked in other sources-to build theoretical understanding and link econometric principles to application.Designed for applied researche.
