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.
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.
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