Using Auxiliary Data to Boost Precision in the Analysis of A/B Tests on an Online Educational Platform: New Data and New Results
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利用辅助数据提高在线教育平台 A/B 测试的分析精度：新数据与新结果
- 研究问题
- 在在线学习平台的 A/B 测试样本较小、效应估计不精确时，如何利用实验外历史用户日志提高总体与子群效应的精度？
- 方法与思路
- 作者回顾结合设计型因果估计与辅助数据机器学习的方法，在超过二百五十组随机比较中评估四种新的深度学习模型。
- 材料陈述的贡献
- 摘要称方法保持无偏与精确推断，相对 t 检验的平均精度增益约等于扩样百分之二十，并可用于子群及后分层总体效应。
- 阅读关注点
- 应核对随机化设计、辅助模型如何进入估计和推断、等效样本增益的计算，以及辅助样本不具代表性时的结论边界。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未核实。摘要未展开精确推断的构造与条件，所报平均增益不能保证每次实验均获益。
原文摘要
Randomized A/B tests within online learning platforms represent an exciting direction in learning sciences. With minimal assumptions, they allow causal effect estimation without confounding bias and exact statistical inference even in small samples. However, often experimental samples and/or treatment effects are small, A/B tests are underpowered, and effect estimates are overly imprecise. Recent methodological advances have shown that power and statistical precision can be substantially boosted by coupling design-based causal estimation to machine-learning models of rich log data from historical users who were not in the experiment. Estimates using these techniques remain unbiased and inference remains exact without any additional assumptions. This paper reviews those methods and applies them to a new dataset including over 250 randomized A/B comparisons conducted within ASSISTments, an online learning platform. We compare results across experiments using four novel deep-learning models of auxiliary data and show that incorporating auxiliary data into causal estimates is roughly equivalent to increasing the sample size by 20\% on average, or as much as 50-80\% in some cases, relative to t-tests, and by about 10\% on average, or as much as 30-50\%, compared to cutting-edge machine learning unbiased estimates that use only data from the experiments. We show that the gains can be even larger for estimating subgroup effects, hold even when the remnant is unrepresentative of the A/B test sample, and extend to post-stratification population effects estimators.