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在观察数据的统计分析中，倾向评分匹配是一种统计匹配技术，它试图通过计算预测接受治疗的协变量来估计治疗、政策或其他干预的效果。PSM 试图减少由于混杂变量造成的偏倚，这些变量可以通过简单地比较接受治疗的单位和没有接受治疗的单位之间的结果来估计治疗效果。保罗 · 罗森鲍姆和唐纳德 · 鲁宾在1983年介绍了这项技术。

在观察数据的统计分析中，倾向评分匹配是一种统计匹配技术，它试图通过计算预测接受治疗的协变量来估计治疗、政策或其他干预的效果。PSM 试图减少由于混杂变量造成的偏倚，这些变量可以通过简单地比较接受治疗的单位和没有接受治疗的单位之间的结果来估计治疗效果。保罗 · 罗森鲍姆和唐纳德 · 鲁宾在1983年介绍了这项技术。

 

The possibility of bias arises because a difference in the treatment outcome (such as the [[average treatment effect]]) between treated and untreated groups may be caused by a factor that predicts treatment rather than the treatment itself. In [[randomized experiment]]s, the randomization enables unbiased estimation of treatment effects; for each covariate, randomization implies that treatment-groups will be balanced on average, by the [[law of large numbers]]. Unfortunately, for observational studies, the assignment of treatments to research subjects is typically not random. [[Matching (statistics)|Matching]] attempts to reduce the treatment assignment bias, and mimic randomization, by creating a sample of units that received the treatment that is comparable on all observed covariates to a sample of units that did not receive the treatment.

The possibility of bias arises because a difference in the treatment outcome (such as the [[average treatment effect]]) between treated and untreated groups may be caused by a factor that predicts treatment rather than the treatment itself. In [[randomized experiment]]s, the randomization enables unbiased estimation of treatment effects; for each covariate, randomization implies that treatment-groups will be balanced on average, by the [[law of large numbers]]. Unfortunately, for observational studies, the assignment of treatments to research subjects is typically not random. [[Matching (statistics)|Matching]] attempts to reduce the treatment assignment bias, and mimic randomization, by creating a sample of units that received the treatment that is comparable on all observed covariates to a sample of units that did not receive the treatment.