Selecting intervals to optimize the design of observational studies subject to fine balance constraints

Motivated by designing observational studies using matching methods subject to fine balance constraints, we introduce a new optimization problem. This problem consists of two phases. In the first phase, the goal is to cluster the values of a continuous covariate into a limited number of intervals. I...

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Bibliographic Details
Published inJournal of combinatorial optimization Vol. 47; no. 3
Main Author Levin, Asaf
Format Journal Article
LanguageEnglish
Published New York Springer US 01.04.2024
Springer Nature B.V
Subjects
Algorithms
Combinatorics
Convex and Discrete Geometry
Design optimization
Intervals
Matching
Mathematical Modeling and Industrial Mathematics
Mathematics
Mathematics and Statistics
Operations Research/Decision Theory
Optimization
Polynomials
Theory of Computation
Fine balance constraints
Matching
Algorithms
Observational studies
Complexity classification
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Summary:Motivated by designing observational studies using matching methods subject to fine balance constraints, we introduce a new optimization problem. This problem consists of two phases. In the first phase, the goal is to cluster the values of a continuous covariate into a limited number of intervals. In the second phase, we find the optimal matching subject to fine balance constraints with respect to the new covariate we obtained in the first phase. We show that the resulting optimization problem is NP-hard. However, it admits an FPT algorithm with respect to a natural parameter. This FPT algorithm also translates into a polynomial time algorithm for the most natural special cases of the problem.
Bibliography:ObjectType-Article-1
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ISSN:1382-6905
1573-2886
DOI:10.1007/s10878-024-01116-y