2021/10/09 by Géza Kiss, Kiss, Géza, Kyle Gorman +3
Computer Science · Mathematics · #Advanced Causal Inference Techniques #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Machine Learning (cs.LG) #Methodology (stat.ME) #Statistical Methods and Inference
paper · pdf · doi:10.48550/arxiv.2110.04432
openalex publication_date 2021/10/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.