A Robust, Differentially Private Randomized Experiment for Evaluating Online Educational Programs With Sensitive Student Data
面向敏感学生数据的稳健差分隐私随机实验AI 解读 ↓
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面向敏感学生数据的稳健差分隐私随机实验
- 研究问题
- 评估在线教育项目时，如何在保护学生敏感结果数据的同时，应对参与者故意污染回答的行为，并估计平均处理效应？
- 方法与思路
- 作者提出差分隐私实验设计，结合不依从研究处理对抗性回答，构造效应估计量，并利用处理前协变量进行双重稳健调整。
- 材料陈述的贡献
- 摘要称该设计下可获得无偏且渐近正态的平均处理效应估计，并提出提升效率的协变量调整方法及在线统计课程应用。
- 阅读关注点
- 应核对隐私机制及预算、对抗性参与者的行为限制、效应识别条件，以及双重稳健性质和隐私引入的精度损失如何成立。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实。摘要未说明隐私参数、污染比例限制或课程效果数值，不能判断具体稳健程度。
原文摘要
Randomized control trials (RCTs) have been the gold standard to evaluate the effectiveness of a program, policy, or treatment on an outcome of interest. However, many RCTs assume that study participants are willing to share their (potentially sensitive) data, specifically their response to treatment. This assumption, while trivial at first, is becoming difficult to satisfy in the modern era, especially in online settings where there are more regulations to protect individuals' data. The paper presents a new, simple experimental design that is differentially private, one of the strongest notions of data privacy. Also, using works on noncompliance in experimental psychology, we show that our design is robust against "adversarial" participants who may distrust investigators with their personal data and provide contaminated responses to intentionally bias the results of the experiment. Under our new design, we propose unbiased and asymptotically Normal estimators for the average treatment effect. We also present a doubly robust, covariate-adjusted estimator that uses pre-treatment covariates (if available) to improve efficiency. We conclude by using the proposed experimental design to evaluate the effectiveness of online statistics courses at the University of Wisconsin-Madison during the Spring 2021 semester, where many classes were online due to COVID-19.