Description

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. A diverse range of examples and exercises provide hands-on experience and exposure to the sort of real data and questions being analyzed at the frontier of many fields. In approachable language that never sacrifices technical rigor, this text equips graduate students and researchers to apply state-of-the art microeconometrics 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 heterogenity analysis
Features a breadth of real-world examples from recent papers
Includes coding implementation in Python, R and Stata