Applied Microeconometrics
Book Details
Format
Paperback / Softback
ISBN-10
0262053640
ISBN-13
9780262053648
Publisher
MIT Press Ltd
Imprint
MIT Press
Country of Manufacture
GB
Country of Publication
GB
Publication Date
Jun 9th, 2026
Print length
384 Pages
Weight
369 grams
Product Classification:
Economics
Ksh 13,700.00
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A rigorous, cutting-edge overview of the range of methods used to conduct causal inference in the social sciences.
This textbook provides a lucid, rigorous, and cutting-edge overview of the methods used to conduct causal inference in the social sciences, covering all the core techniques and latest advances. Offering a detailed survey of the current state of microeconometric theory, Damian Clarke delves deeply into machine learning applications and presents developments in difference-in-difference methods, instrumental variables, multiple hypothesis testing, and other advanced topics. With a diverse range of examples and exercises offering hands-on experience, Applied Microeconometrics equips graduate students and researchers to apply state-of-the art scholarship to actionable problems.
This textbook provides a lucid, rigorous, and cutting-edge overview of the methods used to conduct causal inference in the social sciences, covering all the core techniques and latest advances. Offering a detailed survey of the current state of microeconometric theory, Damian Clarke delves deeply into machine learning applications and presents developments in difference-in-difference methods, instrumental variables, multiple hypothesis testing, and other advanced topics. With a diverse range of examples and exercises offering hands-on experience, Applied Microeconometrics equips graduate students and researchers to apply state-of-the art scholarship to actionable problems.
- Integrates a rich array of machine learning methods into causal modeling frameworks
- Covers recent advances in difference-in-differences and dynamic research designs, formal discussions of challenges related to inference and hypothesis testing, and heterogeneity analysis
- Features a breadth of real-world examples from recent papers
- Includes coding implementation in Python, R, and Stata
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