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Unpublished Paper
A Guide to Quasi-Experimental Designs
(2013)
  • Matt Bogard, Western Kentucky University
Abstract
Linear regression is a very powerful empirical tool that allows for controlled comparisons of treatment effects across groups. However, omitted variable bias, selection bias, and issues related to unobserved heterogeneity and endogeneity can bias standard regression results. Quasi-experimental designs including propensity score methods, instrumental variables, regression discontinuity, and difference-in-difference estimators offer an inferentially rigorous alternative for program evaluation. In this guide, I begin with an introduction to the potential outcomes framework for rigorously characterizing selection bias and follow with discussions of quasi-experimental methods that may be useful to practitioners involved in program evaluation.
Keywords
  • quasi-experimental design,
  • causal inference,
  • econometrics,
  • instrumental variables,
  • propensity score matching,
  • selection bias,
  • difference-in-difference,
  • regression discontinuity
Publication Date
Fall 2013
Citation Information
Matt Bogard. "A Guide to Quasi-Experimental Designs" (2013)
Available at: http://works.bepress.com/matt_bogard/24/