Inferring Optimal Peer Assignment From Experimental Data
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从实验数据推断最优同伴分配
- 研究问题
- 在同伴效应具有异质性时，如何根据实验数据分配同伴以优化平均或分位数结果，并评估分配约束造成的效率损失？
- 方法与思路
- 作者利用大学新生室友随机分配数据估计分配规则，结合数学规划研究最优解及最优值的大样本推断。
- 材料陈述的贡献
- 摘要报告不同室友分配对社团参与和成绩分布的影响，并称推断方法可扩展到分位数等非线性、拟凸的政策目标。
- 阅读关注点
- 应核对随机分配如何支持反事实配对评估、优化所需限制、分位数目标的处理，以及最优解与最优值的推断条件。
- 阅读边界
- 来源为稿件版本未注明的OpenAlex索引摘要；所述分配结果仅涉及该样本和目标，不能推及其他院校或政策环境。
原文摘要
This article studies the problem of optimally dividing individuals into peer groups to maximize social gains from heterogeneous peer effects. The specific setting analyzed here concerns efficient ways of allocating roommates in college dormitories. Using confidential data on a sample of Dartmouth College freshmen who were randomly assigned to be roommates, the article derives efficient roommate pairing rules, based on demographic and academic background, which maximize different aggregate outcomes. Segregation by precollege academic standing and by race are seen to minimize mean enrolment into sororities and maximize mean enrolment into fraternities. Segregation has no effect on mean and median freshman year grade point average (GPA) but increases the higher and decreases the lower percentiles of the GPA distribution for both men and women. Efficiency loss due to legal constraints on allocations (e.g., race-blindness) is shown to be significant and also larger for women for whom peer effects are more nonlinear. The article develops large-sample inference methods for these optimal solutions and the resulting optimized values by using and extending insights from the mathematical programming literature. Applicability of these techniques extends beyond linear maximands such as the mean to other important policy objectives such as outcome quantiles, which, though nonlinear, are shown to be quasi-convex in the allocation probabilities.