Stronger instruments via integer programming in an observational study of late preterm birth outcomes
Jos\'e R. Zubizarreta, Dylan S. Small, Neera K. Goyal, Scott Lorch,, Paul R. Rosenbaum

TL;DR
This paper introduces integer programming techniques to improve nonbipartite matching for instrumental variable analysis in observational studies, exemplified by a study on late preterm birth outcomes in California.
Contribution
It develops new tools for nonbipartite matching using integer programming, enabling fine balance and other constraints not possible with traditional network optimization methods.
Findings
Integer programming allows for more flexible matching constraints.
Application to late preterm birth shows potential for causal inference.
New matching methods improve covariate balance in observational studies.
Abstract
In an optimal nonbipartite match, a single population is divided into matched pairs to minimize a total distance within matched pairs. Nonbipartite matching has been used to strengthen instrumental variables in observational studies of treatment effects, essentially by forming pairs that are similar in terms of covariates but very different in the strength of encouragement to accept the treatment. Optimal nonbipartite matching is typically done using network optimization techniques that can be quick, running in polynomial time, but these techniques limit the tools available for matching. Instead, we use integer programming techniques, thereby obtaining a wealth of new tools not previously available for nonbipartite matching, including fine and near-fine balance for several nominal variables, forced near balance on means and optimal subsetting. We illustrate the methods in our on-going…
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