A Euclidean distance-based matching procedure for non randomized comparison studies

For intervention programs that are applied in natural settings, randomization often is difficult or impossible to achieve. If treated individuals are compared with individuals from a nonrandomized comparison group, the inference of causality can be biased. Similar distributions in the relevant characteristics of the treatment and the comparison groups cannot be expected. To adjust between-group comparisons for preexisting differences, this article proposes a simple matching procedure. This procedure involves pairing of treatment and comparison individuals based on observable characteristics, using Euclidean distance scores. Application of the proposed Euclidean-distance matching (EuM) procedure to data from the Viennese E-Lecturing (VEL) project yields satisfying results. Possible generalizations and applications of the EuM procedure are discussed.