Work

Matching Methods in Practice: Three Examples

Year: 2015

Type: article

Abstract: There is a large theoretical literature on methods for estimating causal effects under unconfoundedness, exogeneity, or selection-on-observables type assumptions using matching or propensity score methods. Much of this literature is highly technical and has not made inroads into empirical practice where many researchers continue to use simple methods such as ordinary least squares regression even in settings where those methods do not have attractive properties. In this paper, I discuss some of the lessons for practice from the theoretical literature and provide detailed recommendations on what to do. I illustrate the recommendations with three detailed applications.

Source: The Journal of Human Resources

DOI: 10.3368/jhr.50.2.373

Author: Guido W. Imbens

Institutions: National Bureau of Economic Research, Stanford University

Language: English

Topic: Advanced Causal Inference Techniques

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: bronze

Funder: National Science Foundation

Awards: Collaborative Research: The Econometrics of Reallocations in the Presence of Complementarity and Social Spillovers: Estimands, Identification and Estimation, `Collaborative Research: Estimation for and Inference on Causal Effects