We compare the performance of standard nearest-neighbor propensity score matching with that of an analogous Bayesian propensity score matching procedure. We show that the Bayesian approach makes better use of available information, as it makes less arbitrary decisions about which observations to drop and which ones to keep in the matched sample. We conduct a simulation study to evaluate the performance of standard and Bayesian nearest-neighbor matching when matching is done with and without replacement. We then use both methods to replicate a recent study about the impact of land reform on guerrilla activity in Colombia.

翻译：我们比较了与类似的贝叶斯人倾向性评分匹配的标准近邻人倾向性评分的绩效。我们表明,贝叶斯方法更好地利用了现有信息,因为它对哪些观测要下降以及哪些观测要保留在匹配的样本中作出了较少武断的决定。我们进行了模拟研究,以评估标准与巴伊斯人近邻性比对的绩效,同时进行与和不替换的比对。然后,我们用两种方法复制最近关于土地改革对哥伦比亚游击队活动的影响的研究。