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Causal Inference and Machine Learning: In Economics, Social, and Health Sciences - Couverture rigide
Synopsis
Bridges gap between modern machine learning methods and applied needs of economists, public health researchers, social scientists. Designed with students and practitioners in mind, introduces machine learning through causal inference. Offers a rigorous yet accessible roadmap for using data to answer real-world policy questions.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
À propos de l'auteur
Mutlu Yuksel is a Professor of Economics at Dalhousie University, Canada, and an applied microeconomist whose research spans labor, health, and development. His recent work applies machine learning and high-dimensional data to complex policy questions. He has received teaching awards and co-founded the ML Portal to support research and training in social and health policy.
Yigit Aydede is the Sobey Professor of Economics at Saint Mary's University, Canada, and an applied economist working at the intersection of econometrics, machine learning, and AI. He teaches data analytics and serves as Faculty in Residence at the Sobey School of Business and as an Affiliate Scientist at Nova Scotia Health. Aydede is also the co-founder of Novastorms.ai and the ML Portal, both focused on data-driven public policy and health research.
À propos de la quatrième de couverture
Bridges gap between modern machine learning methods and applied needs of economists, public health researchers, social scientists. Designed with students and practitioners in mind, introduces machine learning through causal inference. Offers a rigorous yet accessible roadmap for using data to answer real-world policy questions.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Causal Inference and Machine Learning: In Economics, Social, and Health Sciences
Edité par
Chapman and Hall/CRC, 2025
ISBN 10 : 1032820411
ISBN 13 : 9781032820415
Ancien ou d'occasion
Couverture rigide
Vendeur : Books From California, Simi Valley, CA, Etats-Unis
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hardcover. Etat : Fine. N° de réf. du vendeur mon0004115725
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EUR 95,36
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Expédition nationale : Etats-Unis
Expédition nationale : Etats-Unis
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Causal Inference and Machine Learning : In Economics, Social, and Health Sciences
Edité par
Chapman and Hall/CRC, 2025
ISBN 10 : 1032820411
ISBN 13 : 9781032820415
Ancien ou d'occasion
Couverture rigide
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
Évaluation du vendeur 5 sur 5 étoiles
Etat : As New. Unread book in perfect condition. N° de réf. du vendeur 50376479
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EUR 130,75
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Expédition nationale : Etats-Unis
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Causal Inference and Machine Learning : In Economics, Social, and Health Sciences
Edité par
Chapman and Hall/CRC, 2025
ISBN 10 : 1032820411
ISBN 13 : 9781032820415
Neuf
Couverture rigide
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. N° de réf. du vendeur 50376479-n
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Expédition nationale : Etats-Unis
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Causal Inference and Machine Learning (Hardcover)
Edité par
Taylor & Francis Ltd, 2025
ISBN 10 : 1032820411
ISBN 13 : 9781032820415
Neuf
Couverture rigide
Vendeur : Grand Eagle Retail, Bensenville, IL, Etats-Unis
Évaluation du vendeur 5 sur 5 étoiles
Hardcover. Etat : new. Hardcover. 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 learning 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 whyboth methodologically and computationally. Unlike many texts that rely on prebuilt 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 decisionmaking.KEY FEATURESIntegrates causal inference with the latest econometric and machine learning methods to address realworld policy questions in economics, health, and the social sciences.Offers clear, detailed explanations and intuitive guidanceeven for foundational concepts often overlooked in other sourcesto build theoretical understanding and link econometric principles to application.Designed for applied researchers, students, and practitioners with limited technical background, with step-by-step instruction from raw data and basic code, including how both the methods and the underlying code function.Provides practical guidance on when and how to use predictive vs. causal models, highlighting their trade-offs and pitfalls to avoid, supported by real-world examples and simulation-based demonstrations. Bridges gap between modern machine learning methods and applied needs of economists, public health researchers, social scientists. Designed with students and practitioners in mind, introduces machine learning through causal inference. Offers a rigorous yet accessible roadmap for using data to answer real-world policy questions. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9781032820415
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EUR 136,02
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Expédition nationale : Etats-Unis
Expédition nationale : Etats-Unis
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Causal Inference and Machine Learning: In Economics, Social, and Health Sciences
Edité par
Chapman and Hall/CRC, 2025
ISBN 10 : 1032820411
ISBN 13 : 9781032820415
Neuf
Couverture rigide
Vendeur : California Books, Miami, FL, Etats-Unis
Évaluation du vendeur 4 sur 5 étoiles
Etat : New. N° de réf. du vendeur I-9781032820415
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EUR 136,05
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Causal Inference and Machine Learning
Edité par
Chapman and Hall/CRC, 2025
ISBN 10 : 1032820411
ISBN 13 : 9781032820415
Neuf
Couverture rigide
Vendeur : Majestic Books, Hounslow, Royaume-Uni
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Etat : New. N° de réf. du vendeur 409787013
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EUR 139,94
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Expédition depuis Royaume-Uni vers Etats-Unis
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Causal Inference and Machine Learning : In Economics, Social, and Health Sciences
Edité par
Chapman and Hall/CRC, 2025
ISBN 10 : 1032820411
ISBN 13 : 9781032820415
Ancien ou d'occasion
Couverture rigide
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Etat : As New. Unread book in perfect condition. N° de réf. du vendeur 50376479
Acheter D'occasion
EUR 131,03
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Expédition depuis Royaume-Uni vers Etats-Unis
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Causal Inference and Machine Learning
Vendeur : PBShop.store UK, Fairford, GLOS, Royaume-Uni
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HRD. Etat : New. New Book. Shipped from UK. Established seller since 2000. N° de réf. du vendeur L2-9781032820415
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EUR 139,46
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Causal Inference and Machine Learning
Vendeur : PBShop.store US, Wood Dale, IL, Etats-Unis
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HRD. Etat : New. New Book. Shipped from UK. Established seller since 2000. N° de réf. du vendeur L2-9781032820415
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EUR 151
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Image d'archives
Causal Inference and Machine Learning : In Economics, Social, and Health Sciences
Edité par
Chapman and Hall/CRC, 2025
ISBN 10 : 1032820411
ISBN 13 : 9781032820415
Neuf
Couverture rigide
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. N° de réf. du vendeur 50376479-n
Acheter neuf
EUR 139,45
Expédition à EUR 17,50
Expédition depuis Royaume-Uni vers Etats-Unis
Expédition depuis Royaume-Uni vers Etats-Unis
Quantité disponible : 10 disponible(s)