The Balance-Sample Size Frontier in Matching Methods for Causal Inference
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Author(s)
King, Gary
Lucas, Christopher
Nielsen, Richard Alexander
Date Issued
November 2016
Journal
American Journal of Political Science
Publisher
Wiley
Citation
King, Gary, Christopher Lucas, and Richard A. Nielsen. “The Balance-Sample Size Frontier in Matching Methods for Causal Inference.” American Journal of Political Science 61, no. 2 (November 9, 2016): 473–489.
Version
Author's final manuscript
Abstract
We propose a simplified approach to matching for causal inference that simultaneously optimizes balance (similarity between the treated and control groups) and matched sample size. Existing approaches either fix the matched sample size and maximize balance or fix balance and maximize sample size, leaving analysts to settle for suboptimal solutions or attempt manual optimization by iteratively tweaking their matching method and rechecking balance. To jointly maximize balance and sample size, we introduce the matching frontier, the set of matching solutions with maximum possible balance for each sample size. Rather than iterating, researchers can choose matching solutions from the frontier for analysis in one step. We derive fast algorithms that calculate the matching frontier for several commonly used balance metrics. We demonstrate this approach with analyses of the effect of sex on judging and job training programs that show how the methods we introduce can extract new knowledge from existing data sets.
MIT Department
Massachusetts Institute of Technology. Department of Political Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1111/AJPS.12272
