We introduce profile matching, a multivariate matching method for randomized experiments and observational studies that finds the largest possible unweighted samples across multiple treatment groups that are balanced relative to a covariate profile. This covariate profile can represent a specific population or a target individual, facilitating the generalization and personalization of causal inferences. For generalization, because the profile often amounts to summary statistics for a target population, profile matching does not always require accessing individual-level data, which may be unavailable for confidentiality reasons. For personalization, the profile comprises the characteristics of a single individual. Profile matching achieves covariate balance by construction, but unlike existing approaches to matching, it does not require specifying a matching ratio, as this is implicitly optimized for the data. The method can also be used for the selection of units for study follow-up, and it readily applies to multi-valued treatments with many treatment categories. We evaluate the performance of profile matching in a simulation study of the generalization of a randomized trial to a target population. We further illustrate this method in an exploratory observational study of the relationship between opioid use and mental health outcomes. We analyze these relationships for three covariate profiles representing: (i) sexual minorities, (ii) the Appalachian United States, and (iii) the characteristics of a hypothetical vulnerable patient. The method can be implemented via the new function profmatch in the designmatch package for R, for which we provide a step-by-step tutorial.

翻译：我们引入了配置匹配, 即随机实验和观察研究的多变量匹配方法, 发现在多个处理组之间最大可能的未加权样本, 这些样本与共变剖面相比是平衡的。 共变剖面可以代表特定人群或目标个人, 有利于因果推断的概括化和个性化。 对于概括化而言, 因为剖面通常相当于目标人群的汇总统计数据, 配置匹配并不总是需要获取个人层面的数据, 这些数据可能因保密原因而无法获得 。 对于个人化而言, 剖面配置包含单个个体的特征。 配置匹配通过构建实现共变平衡, 但与现有的匹配方法不同, 它并不要求指定匹配比例, 因为这对数据是暗含的优化 。 该方法也可以用于选择用于研究后续的单位, 并且很容易适用于多个治疗类别中的多价治疗。 我们评估剖面配置的匹配性测试工作绩效, 对目标人群进行模拟的随机测试。 我们进一步展示了这一方法, 对类阿片使用和心理健康结果之间的关系进行探索性研究, 但与现有方法不同, 我们分析这些关系, 用于三个共同设计方法 的性别剖面图 。