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Product Identifiers
PublisherMIT Press
ISBN-100262545918
ISBN-139780262545914
eBay Product ID (ePID)20058364064
Product Key Features
Book TitleCausal Analysis : Impact Evaluation and Causal Machine Learning with Applications in R
Number of Pages336 Pages
LanguageEnglish
Publication Year2023
TopicProbability & Statistics / General, General, Econometrics
GenreMathematics, Social Science, Business & Economics
AuthorMartin Huber
FormatTrade Paperback
Dimensions
Item Height0.8 in
Item Weight21.9 Oz
Item Length9 in
Item Width7 in
Additional Product Features
Intended AudienceTrade
LCCN2022-033285
Table Of Content1 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 311
SynopsisPresenting 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 causa! 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, 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
LC Classification NumberH62.H713 2023
