This paper considers the sensitivity of Genetic Matching (GenMatch) to the choice of balance measure. It explores the performance of a newly introduced distributional balance measure that is similar to the KS test but is more evenly sensitive to imbalance across the support. This measure is introduced by Goldman & Kaplan (2008) (i.e. the GK measure). This is important because the rationale behind distributional balance measures is their ability to provide a broader description of balance. I also consider the performance of multivariate balance measures i.e. distance covariance and correlation. This is motivated by the fact that ideally, balance for causal inference refers to balance in joint density and individual balance in a set of univariate distributions does not necessarily imply balance in the joint distribution.

Simulation results show that GK dominates the KS test in terms of Bias and Mean Square Error (MSE); and the distance correlation measure dominates all other measure in terms of Bias and MSE. These results have two important implication for the choice of balance measure (i) Even sensitivity across the support is important and not all distributional measures has this property (ii) Multivariate balance measures can improve the performance of matching estimators.