论文标题
因果推理的无调参数最佳倾向得分匹配方法
Tuning-parameter-free optimal propensity score matching approach for causal inference
论文作者
论文摘要
倾向得分匹配（PSM）是一种伪实验方法，它使用统计技术通过将每个处理的单元与一个或多个未经处理的类似特性的单位匹配来构建人工对照组。迄今为止，确定在PSM中起重要作用的每单位匹配数量的最佳数量的问题尚未得到充分解决。我们提出了一种基于单调性约束下倾向得分的非参数最大样本估计的无调PSM方法。估计的倾向得分是分段常数的，因此自动分组数据。因此，我们的建议没有调整参数。提出的估计量在单变量的情况下是渐近的半参数有效的，并且在结果和倾向得分取决于在相同方向上的协变量时，在多变量情况下达到了这种效率。我们得出的结论是，通常基于倾向得分的匹配方法通常不能有效。
Propensity score matching (PSM) is a pseudo-experimental method that uses statistical techniques to construct an artificial control group by matching each treated unit with one or more untreated units of similar characteristics. To date, the problem of determining the optimal number of matches per unit, which plays an important role in PSM, has not been adequately addressed. We propose a tuning-parameter-free PSM method based on the nonparametric maximum-likelihood estimation of the propensity score under the monotonicity constraint. The estimated propensity score is piecewise constant, and therefore automatically groups data. Hence, our proposal is free of tuning parameters. The proposed estimator is asymptotically semiparametric efficient for the univariate case, and achieves this level of efficiency in the multivariate case when the outcome and the propensity score depend on the covariate in the same direction. We conclude that matching methods based on the propensity score alone cannot, in general, be efficient.
