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OverviewA comprehensive and cutting-edge introduction to quantitative methods of causal analysis, including new trends in machine learning. A comprehensive and cutting-edge introduction to quantitative methods of causal analysis, including new trends in machine learning. Reasoning about cause and effect-the consequence of doing one thing versus another-is an integral part of our lives as human beings. In an increasingly digital and data-driven economy, the importance of sophisticated causal analysis only deepens. Presenting the most important quantitative methods for evaluating causal effects, this textbook provides graduate students and researchers with a clear and comprehensive introduction to the causal analysis of empirical data. Martin Huber's accessible approach highlights the intuition and motivation behind various methods while also providing formal discussions of key concepts using statistical notation. Causal Analysis covers several methodological developments not covered in other texts, including new trends in machine learning, the evaluation of interaction or interference effects, and recent research designs such as bunching or kink designs. Most complete and cutting-edge introduction to causal analysis, including causal machine learning Clean presentation of rigorous material avoids extraneous detail and emphasizes conceptual analogies over statistical notation Supplies a range of applications and practical examples using R Full Product DetailsAuthor: Martin HuberPublisher: MIT Press Ltd Imprint: MIT Press Weight: 0.369kg ISBN: 9780262545914ISBN 10: 0262545918 Pages: 336 Publication Date: 01 August 2023 Audience: General/trade , General Format: Paperback Publisher's Status: Active Availability: To order  Stock availability from the supplier is unknown. We will order it for you and ship this item to you once it is received by us. Table of Contents1 Introduction 1 2 Causality and No Causality 11 3 Social Experiments and Linear Regression 19 4 Selection on Observables 65 5 Casual Machine Learning 137 6 Instrumental Variables 169 7 Difference-in-Differences 195 8 Synthetic Controls 219 9 Regression Discontinuity, Kink, and Bunching Designs 231 10 Partial Identification and Sensitivity Analysis 255 11 Treatment Evaluation under Interference Effects 271 12 Conclusion 285 References 287 Index 311ReviewsAuthor InformationMartin Huber is Professor of Applied Econometrics at the University of Fribourg, Switzerland, where his research comprises both methodological and applied contributions in the fields of causal analysis and policy evaluation, machine learning, statistics, econometrics, and empirical economics. Tab Content 6Author Website:Countries AvailableAll regions |
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