Imposing Minimax and Quantile Constraints on Optimal Matching in Observational Studies

• Published in: Journal of computational and graphical statistics Vol. 26; no. 1; pp. 66 - 78
• Main Author: Rosenbaum, Paul R.
• Format: Journal Article
• Language: English
• Published: Alexandria Taylor & Francis 02.01.2017; Taylor & Francis Ltd
• Subjects: Algorithms; Assignment problem; Bottleneck assignment; Bottlenecks; Fine balance; Labor economics; Statistical methods; Studies; Threshold algorithm
• ISSN: 1061-8600; 1537-2715
• DOI: 10.1080/10618600.2016.1152971

Modern methods construct a matched sample by minimizing the total cost of a flow in a network, finding a pairing of treated and control individuals that minimizes the sum of within-pair covariate distances subject to constraints that ensure distributions of covariates are balanced. In aggregate, these methods work well; however, they can exhibit a lack of interest in a small number of pairs with large covariate distances. Here, a new method is proposed for imposing a minimax constraint on a minimum total distance matching. Such a match minimizes the total within-pair distance subject to various constraints including the constraint that the maximum pair difference is as small as possible. In an example with 1391 matched pairs, this constraint eliminates dozens of pairs with moderately large differences in age, but otherwise exhibits the same excellent covariate balance found without this additional constraint. A minimax constraint eliminates edges in the network, and can improve the worst-case time bound for the performance of the minimum cost flow algorithm, that is, a better match from a practical perspective may take less time to construct. The technique adapts ideas for a different problem, the bottleneck assignment problem, whose sole objective is to minimize the maximum within-pair difference; however, here, that objective becomes a constraint on the minimum cost flow problem. The method generalizes. Rather than constrain the maximum distance, it can constrain an order statistic. Alternatively, the method can minimize the maximum difference in propensity scores, and subject to doing that, minimize the maximum robust Mahalanobis distance. An example from labor economics is used to illustrate. Supplementary materials for this article are available online.