论文标题
匹配范围：匹配算法的选择如何影响治疗效果估计以及该如何处理
Matching Bounds: How Choice of Matching Algorithm Impacts Treatment Effects Estimates and What to Do about It
论文作者
论文摘要
社会科学领域的许多主要作品都采用匹配来得出因果结论，但是同一数据上的不同匹配也可能产生不同的治疗效果估计，即使它们达到相似的平衡或最小化相同的损失函数。我们讨论了这个问题的原因和后果。我们通过复制使用匹配的十篇论文来提供有关此问题的证据，我们发现不同流行的匹配算法会产生不一致的结果。我们介绍了匹配边界：一种有限样本的无障碍方法，该方法允许分析师知道是否可以从其数据中获得具有相同水平的平衡和整体匹配质量的匹配样本。我们将匹配界限应用于两项研究的复制，并表明在一个情况下，结果对此问题是可靠的，而在另一个情况下则不是。
Many major works in social science employ matching to make causal conclusions, but 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 and we find that different popular matching algorithms produce inconsistent results. We introduce Matching Bounds: a finite-sample, nonstochastic 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 two studies and show that in one case results are robust to this issue and in another they are not.
