Applied Microeconometrics

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

Damian Clarke is Associate Professor of Economics at the University of Exeter and the University of Chile, Data Editor for the journals of the UK’s Royal Economic Society, and coauthor of Microeconometrics and MATLAB: An Introduction.