CUPED on Steroids: Multivariate Covariate Adjustment for Switchback Experiments
强化 CUPED：切换实验中的多变量协变量调整AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要直接研究随机实验中的处理效应估计与协变量调整，重点是估计精度和实验功效，因而归入因果推断。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
关注这篇论文的作者
强化 CUPED：切换实验中的多变量协变量调整
- 研究问题
- 在切换实验及相关聚类随机设计中，如何利用更丰富的实验前信息降低处理效应估计方差，同时控制过拟合与协变量泄漏？
- 方法与思路
- 作者扩展 CUPED，结合多个历史滞后、小时周期编码及聚类和小时固定效应，并通过交叉拟合控制样本内过拟合。
- 材料陈述的贡献
- 摘要称，该框架在模拟和纽约出租车记录应用中提高方差降低效率与统计功效，并推导任意协变量调整可达到的方差降低上限。
- 阅读关注点
- 核对切换与聚类随机设计的具体设定、协变量构造和交叉拟合划分，以及方差降低上限如何取决于结果变异的组成。
- 阅读边界
- 这是 arXiv 预印本，同行评审及接收未核实；摘要未说明 CUPED 隐含假设的具体内容、效率增益数值及上限成立条件。
原文摘要
Controlled-experiment Using Pre-Experiment Data (CUPED) reduces the variance of the treatment effect estimator in online experiments by adjusting the in-experiment outcome metric using its lagged pre-experiment value. This method can be strengthened by enriching its covariate set while keeping it automatable and guarding against overfitting and covariate leakage. In switchback and related clustered experiment designs, this enrichment may be especially fruitful since randomization-unit-level variation in the outcome amplifies the variance of the treatment effect estimator, so covariates that explain it yield disproportionately large efficiency gains. We develop an extended CUPED framework for switchback and related clustered designs that introduces sizeable improvement in variance reduction efficiency relative to conventional CUPED while remaining lightweight and free of assumptions beyond those already implicit in CUPED. The framework combines multiple historical lags with cyclic hour-of-day encodings and cluster- and hour-level fixed-effect dummies in the pre-period prediction model---covariates that target randomization-unit-level outcome variation---and uses cross-fitting so that realized variance reduction is not inflated by in-sample overfitting. In simulation, we analyze how each component affects validity and efficiency of this method and show that the full framework yields large gains in statistical power. We further validate the approach on publicly available trip records from the New York City Taxi and Limousine Commission. Finally, we derive a closed-form ceiling on the variance reduction attainable by any covariate adjustment and connect our empirical findings to the composition-dependent limits this ceiling imposes.