Matching Bounds: How Choice of Matching Algorithm Impacts Treatment Effects Estimates and What to Do about It
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Different matches on the same data may produce different treatment effect estimates, even when they achieve similar balance or minimize the same loss function. We discuss reasons and consequences of this problem. We present evidence of this problem by replicating ten papers that use matching. We introduce Matching Bounds: a finite-sample, non-stochastic method that allows analysts to know whether a matched sample that produces different results with the same levels of balance and overall match quality could be obtained from their data. We apply Matching Bounds to a replication of a matching study of the effects of foreign aid on civil conflict and find that results could change based on the matched sample chosen.
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