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Beschreibung
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.

  • 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
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.

  • 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
Über den Autor
Damian Clarke
Details
Erscheinungsjahr: 2026
Fachbereich: Volkswirtschaft
Genre: Importe, Wirtschaft
Rubrik: Recht & Wirtschaft
Medium: Taschenbuch
ISBN-13: 9780262053648
ISBN-10: 0262053640
Sprache: Englisch
Einband: Kartoniert / Broschiert
Autor: Clarke, Damian
Hersteller: MIT Press Ltd
Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, D-36244 Bad Hersfeld, gpsr@libri.de
Maße: 226 x 202 x 26 mm
Von/Mit: Damian Clarke
Erscheinungsdatum: 09.06.2026
Gewicht: 0,848 kg
Artikel-ID: 136367351

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