Group-matching algorithms for subjects and items
This addresses the need for improved group-matching methods in observational research where traditional pair-matching is inadequate.
The paper tackles the NP-hard problem of constructing statistically similar matched groups for multiple covariates in quasi-experimental studies, showing that heuristics in the ldamatch package produce high-quality matches on artificial and real-world datasets.
We consider the problem of constructing matched groups such that the resulting groups are statistically similar with respect to their average values for multiple covariates. This group-matching problem arises in many cases, including quasi-experimental and observational studies in which subjects or items are sampled from pre-existing groups, scenarios in which traditional pair-matching approaches may be inappropriate. We consider the case in which one is provided with an existing sample and iteratively eliminates samples so that the groups "match" according to arbitrary statistically-defined criteria. This problem is NP-hard. However, using artificial and real-world data sets, we show that heuristics implemented by the ldamatch package produce high-quality matches.