Science is concerned with identifying causal inferences. To move beyond simple observed relationships and associational inferences, researchers may employ randomized experimen-tal designs to isolate a treatment effect, which then per-mits causal inferences. When experiments are not prac-tical, a researcher is relegated to analyzing observational data. To make causal inferences from observational data, one must adjust the data so that they resemble data that might have emerged from an experiment. Traditionally, this has occurred through statistical models identified as match-ing methods. We claim that matching methods are unnecessarily constraining and propose, instead, that the goal is better achieved via a subset selection procedure that is able to identify statistically indistinguishable treatment and control groups. This reformulation to identifying optimal subsets leads to a model that is computationally complex. We develop an evolutionary algorithm that is more efficient and identifies empirically more optimal solutions than any other causal inference method. To gain greater efficiency, we also develop a scalable algorithm for a parallel computing environment by enlisting additional processors to search a greater range of the solution space and to aid other processors at particularly difficult peaks.