What Can We Learn from Student-Performance Measures? Identifying Treatment in the Presence of Curves and Letter Grades
研究了成绩曲线和字母等级转换如何系统性地干扰教育政策干预效果的因果识别，发现这些转换会导致处理效应估计出现偏差，例如高绩效学生的响应被低估、低绩效学生的响应被高估。
Grade-based performance measures are often relied on when considering the efficacy of education-related policy interventions. Yet, it is common for measures of student performance to be subjected to curves and discretized through letter-grade transformations. We show how transformed grades systematically challenge causal identification. Even without explicit curving, transformations to letter grade are particularly problematic and yield treatment estimates that are weighted combinations of inflated responsiveness around letter thresholds and “zeros” away from these thresholds. Curving practices can also introduce false patterns of treatment heterogeneity, attenuating measured responses to treatment among high-performing students, for example, or inflating measured responses among low-performing students.