Beyond Truth-Telling: Preference Estimation with Centralized School Choice and College Admissions
提出新方法，利用匹配机制（特别是Gale-Shapley延迟接受算法）的数据估计学生偏好，假设稳定性比真实陈述更合理，并通过巴黎择校数据验证。
We propose novel approaches to estimating student preferences with data from matching mechanisms, especially the Gale-Shapley deferred acceptance. Even if the mechanism is strategy-proof, assuming that students truthfully rank schools in applications may be restrictive. We show that when students are ranked strictly by some ex ante known priority index (e.g., test scores), stability is a plausible and weaker assumption, implying that every student is matched with her favorite school/college among those she qualifies for ex post. The methods are illustrated in simulations and applied to school choice in Paris. We discuss when each approach is more appropriate in real-life settings.