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Branch-and-price algorithms for the graph coloring problem use an exponentially sized independent set-based integer programming formulation to produce usually tight lower bounds to enable more aggressive pruning in the branch-and-bound [...]

Matching is widely used in the estimation of treatment effects in observational studies. However, the matching paradigm may be too restrictive in many cases because exact matches often do not [...]

A network simplex algorithm is described for the minimum-cost network flow problem on a generalized network, with the additional constraint that there exist sets of arcs that must carry equal [...]

The weeks leading up to Election Day are fraught with uncertainty as each candidate attempts to put their best foot forward, distinguishing themselves from the opposing candidate. The hysteria of [...]

Numerous political pundits believe that the 2016 presidential election will be unlike any other election in the past. It is not because of the prevalence of social media or the [...]

When estimating a treatment effect from observational data, researchers encounter bias regardless of estimation methods. In this paper, we focus on a particular method of estimation called Balance Optimization Subset Selection (BOSS). This paper investigates all the possible cases that may lead to bias in the context of BOSS, provides examples for those cases and tries to mitigate the bias. While doing so, we define a balance hierarchy and a correct imbalance measure which corresponds to the form of the response functions. In addition, new imbalance measures drawn from the Cramer-von Mises test statistic are introduced. The cases of insufficient data and suboptimality that can arise in causal analysis with BOSS are also presented. [ABSTRACT FROM AUTHOR]