Genetic matching is an algorithm that iteratively checks propensity scores. It improves them using a combination of propensity score matching and Mahalanobis distance matching (Diamond & Sekhon, 2012).
The jury is out on whether genetic matching is a “better” method than any of the other matching techniques. Genetic matching may theoretically be a better choice if standard methods like full matching or nearest-neighbor greedy matching don’t sufficiently reduce imbalance (Holmes, 2013). Although some authors report good results, others (Holmes, 2013; Colson et al., 2016) either didn’t find any significant difference or reported that greedy matching was a lower performing method or equaled other matching methods.
References:
Colson, K., Rudolph, K., Zimmerman, S. et al. Optimizing matching and analysis combinations for estimating causal effects. Sci Rep 6, 23222 (2016). https://doi.org/10.1038/srep23222
Diamond, A. & Sekhon, J. (2013). Genetic Matching for Estimating Causal Effects: A General Multivariate Matching Method for Achieving Balance in Observational Studies. The Review of Economics and Statistics. July 2013, Vol. 95, No. 3, Pages: 932-945.
Holmes, W. (2013). Using Propensity Scores in Quasi-Experimental Designs. SAGE Publications.