We propose a simplified approach to matching for causal inference that simultaneously optimizes both balance (similarity between the treated and control groups) and matched sample size. Existing appro
Nearest-neighbor matching is a popular nonparametric tool to create balance between treatment and control groups in observational studies. As a preprocessing step before regression, matching reduces t
