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Does the choice of balance-measure matter under Genetic Matching?

In applied studies, the influence of balance measures on the performance of matching estimators is often taken for granted. This paper considers the performance of different balance measures that have been used in the literature when balance is being optimized. We also propose the use of the entropy measure in assessing balance. To examine the effect of balance measures, we conduct a simulation study where we optimize balance using Genetic Algorithm (GenMatch).
We found that balance measures do influence matching estimates under the GenMatch algorithm. The bias and Root Mean Square Error (RMSE) of the estimated treatment effect vary with the choice of balance measure. In the artificial Data Generating Process (DGP) with one covariate considered in this study, the proposed entropy balance measure has the lowest RMSE.
The implication of these results is that sensitivity of matching estimates to the choice of balance measure should be given greater attention in empirical studies.
Working paper 819
1 May 2020
RELATED JOURNAL:

Empirical Economics
14 May 2020
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