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Statistical matching methods are widely used in the social and healthsciences to estimate causal effects using observational data. Often theobjective is to find comparable groups with similar covariate distributions ina dataset, with the aim to reduce bias in a random experiment. We aim todevelop a foundation for deterministic methods which provide results with lowbias, while retaining interpretability. The proposed method matches on thecovariates and calculates all possible maximal exact matchesfor a given datasetwithout adding numerical errors. Notable advantages of our method over existingmatching algorithms are that all available information for exact matches isused, no additional bias is introduced, it can be combined with other matchingmethods for inexact matching to reduce pruning and that the result iscalculated in a fast and deterministic way. For a given dataset the result istherefore provably unique for exact matches in the mathematical sense. Weprovide proofs, instructions for implementation as well as a numerical examplecalculated for comparison on a complete survey.

A deterministic matching method for exact matchings to compare the outcome of different interventions