Replication Data for: Combining Outcome-Based and Preference-Based Matching: A Constrained Priority Mechanism

We introduce a constrained priority mechanism that combines outcome-based matching from machine-learning with preference-based allocation schemes common in market design. Using real-world data, we illustrate how our mechanism could be applied to the assignment of refugee families to host country locations, and kindergarteners to schools. Our mechanism allows a planner to first specify a threshold $\bar g$ for the minimum acceptable average outcome score that should be achieved by the assignment. In the refugee matching context, this score corresponds to the predicted probability of employment, while in the student assignment context it corresponds to standardized test scores. The mechanism is a priority mechanism that considers both outcomes and preferences by assigning agents (refugee families, students) based on their preferences, but subject to meeting the planner's specified threshold. The mechanism is both strategy-proof and constrained efficient in that it always generates a matching that is not Pareto dominated by any other matching that respects the planner's threshold.

本文提出一种受限优先级机制，该机制将机器学习领域的基于结果的匹配方法，与市场设计中常见的基于偏好的分配方案相结合。我们基于真实世界数据，演示了该机制可应用于两类分配场景：难民家庭至东道国安置点的匹配分配，以及幼儿园儿童的入园分配。
本机制允许规划者首先设定阈值$ar g$，作为分配方案需达成的最低可接受平均结果得分。在难民匹配场景中，该得分对应就业预测概率；在学生分配场景中，则对应标准化考试成绩。
该机制属于优先级机制范畴，通过基于参与主体（难民家庭、学生）的偏好开展分配，同时严格满足规划者指定的阈值条件，从而兼顾结果指标与主体偏好。
本机制同时具备防策略操纵（strategy-proof）性与受限有效性：其生成的匹配方案，不会被任何满足规划者阈值要求的其他匹配方案所帕累托劣化。