We consider two recent suggestions for how to perform an empirically motivated Monte Carlo study to help select a treatment effect estimator under unconfoundedness. We show theoretically that neither is likely to be informative except under restrictive conditions that are unlikely to be satisfied in many contexts. To test empirical relevance, we also apply the approaches to a real‐world setting where estimator performance is known. Both approaches are worse than random at selecting estimators that minimize absolute bias. They are better when selecting estimators that minimize mean squared error. However, using a simple bootstrap is at least as good and often better. For now, researchers would be best advised to use a range of estimators and compare estimates for robustness.
Authors
Research Associate University College London and Brown University
Toru is a Research Associate of the IFS, a Professor of Economics at UCL and an Associate Professor in the Department of Economics at Brown University
Research Fellow University of Warwick
Arun is a Research Fellow at IFS, an Associate Professor of Economics at the University of Warwick and a Commissioner at the Wealth Tax Commission.
Tymon Słoczyński
Journal article details
- DOI
- 10.1002/jae.2724
- Publisher
- Wiley
- Issue
- Volume 34, Issue 6, October 2019, pages 893-910
Suggested citation
A, Advani and T, Kitagawa and T, Słoczyński. (2019). 'Mostly harmless simulations? On the internal validity of empirical Monte Carlo studies' 34(6/2019), pp.893–910.
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