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 series of simulation studies 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. While the performance of different balance measures vary across simulation designs, some pattern did emerge. In an artificial data generating process (DGP) with one covariate, the proposed entropy balance measure has the lowest RMSE. However, in more realistic DGPs with many covariates, the standardized difference in means appear to be a very robust measure of balance, this measure either dominate other measures or come in as a close second in terms of bias or/and 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.(This abstract was borrowed from another version of this item.)