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A Fast Algorithm for Maximal Propensity Score Matching

Author

Listed:
  • Pavel S. Ruzankin

    (Sobolev Institute of Mathematics
    Novosibirsk State University)

Abstract

We present a new algorithm which detects the maximal possible number of matched disjoint pairs satisfying a given caliper when a bipartite matching is done with respect to a scalar index (e.g., propensity score), and constructs a corresponding matching. Variable width calipers are compatible with the technique, provided that the width of the caliper is a Lipschitz function of the index. If the observations are ordered with respect to the index then the matching needs O(N) operations, where N is the total number of subjects to be matched. The case of 1-to-n matching is also considered. We offer also a new fast algorithm for optimal complete one-to-one matching on a scalar index when the treatment and control groups are of the same size. This allows us to improve greedy nearest neighbor matching on a scalar index.

Suggested Citation

  • Pavel S. Ruzankin, 2020. "A Fast Algorithm for Maximal Propensity Score Matching," Methodology and Computing in Applied Probability, Springer, vol. 22(2), pages 477-495, June.
    • Handle: RePEc:spr:metcap:v:22:y:2020:i:2:d:10.1007_s11009-019-09718-4
    DOI: 10.1007/s11009-019-09718-4
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