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

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  • Asaf Levin

    (The Technion)

Abstract

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.

Suggested Citation

  • Asaf Levin, 2024. "Selecting intervals to optimize the design of observational studies subject to fine balance constraints," Journal of Combinatorial Optimization, Springer, vol. 47(3), pages 1-16, April.
    • Handle: RePEc:spr:jcomop:v:47:y:2024:i:3:d:10.1007_s10878-024-01116-y
    DOI: 10.1007/s10878-024-01116-y
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    References listed on IDEAS

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    1. Rosenbaum, Paul R. & Ross, Richard N. & Silber, Jeffrey H., 2007. "Minimum Distance Matched Sampling With Fine Balance in an Observational Study of Treatment for Ovarian Cancer," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 75-83, March.
    2. Jason J. Sauppe & Sheldon H. Jacobson & Edward C. Sewell, 2014. "Complexity and Approximation Results for the Balance Optimization Subset Selection Model for Causal Inference in Observational Studies," INFORMS Journal on Computing, INFORMS, vol. 26(3), pages 547-566, August.
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