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ultimatch: Matching counterfactuals your way

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  • Thorsten Doherr

    (Leibniz Centre for European Economic Research)

Abstract

ultimatch implements various score and distance based matching methods, for example, nearest neighbor, radius, coarsened exact, percentile rank and Mahalanobis distance matching. It implements an efficient method for distance-based matching like Mahalanobis matching, preventing the quadratic increment of the runtime. Matched observations are marked individually allowing interactions between treated and counterfactuals. Different methods can be combined to improve the results or to impose external requirements on the matched. Among other control variables, it creates mandatory weights to provide balanced matching results, preventing distortions caused by skewed counterfactual candidate distributions, for instance, overabundance of candidates with the same score or within the same coarsened group.

Suggested Citation

  • Thorsten Doherr, 2019. "ultimatch: Matching counterfactuals your way," London Stata Conference 2019 19, Stata Users Group.
    • Handle: RePEc:boc:usug19:19
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