In a pilot program during the 2016-17 admissions cycle, the University of California, Berkeley invited many applicants for freshman admission to submit letters of recommendation. We use this pilot as the basis for an observational study of the impact of submitting letters of recommendation on subsequent admission, with the goal of estimating how impacts vary across pre-defined subgroups. Understanding this variation is challenging in observational studies, however, because estimated impacts reflect both actual treatment effect variation and differences in covariate balance across groups. To address this, we develop balancing weights that directly optimize for ``local balance'' within subgroups while maintaining global covariate balance between treated and control units. We then show that this approach has a dual representation as a form of inverse propensity score weighting with a hierarchical propensity score model. In the UC Berkeley pilot study, our proposed approach yields excellent local and global balance, unlike more traditional weighting methods, which fail to balance covariates within subgroups. We find that the impact of letters of recommendation increases with the predicted probability of admission, with mixed evidence of differences for under-represented minority applicants.
翻译:在2016-17年入学周期的试点方案中,伯克利大学邀请许多新生入学申请者提交推荐信。我们以这一试点为基础,观察研究提交推荐信对以后入学的影响,目的是估计预先界定的各分组的影响如何不同。然而,了解这一差异在观察研究中具有挑战性,因为估计影响既反映了实际治疗效果的变化,也反映了各分组之间共变平衡的差异。为了解决这个问题,我们开发了平衡权重,直接优化分组内的“地方平衡”内部的平衡,同时保持经处理和控制单位之间的全球共变平衡。然后,我们表明,这一方法具有双重代表性,是一种反倾向性加权法,与等级偏差性偏差分模型相提并论。在UCBerke试点研究中,我们提议的方法与传统的加权方法不同,在分组内无法平衡。我们发现,建议书的影响随着预计的入学概率增加,有代表性不足的少数群体申请人的差异证据混杂。