Juan Díaz; Jorge Rivera
Abstract: This paper proposes a novel matching estimator where neighbors used and weights are endogenously determined by optimizing a covariate balance criterion. The estimator is based on finding, for each unit that needs to be matched, sets of observations such that a convex combination of them has the same covariate values as the unit needing matching, or with minimized distance between them. We implement the proposed estimator to data from the National Supported Work Demonstration finding an outstanding performance in terms of covariate balance. Monte Carlo evidence shows that our estimator performs well in designs previously used in the literature.
Keywords: Matching estimator, treatment eﬀect estimator, non-parametric methods, bi-level optimization
JEL: C01, C14, C61