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摘要翻译：
在线市场设计师经常运行A/B测试来衡量拟议产品更改的影响。然而，由于市场的内在联系，通过伯努利随机实验得到的总平均治疗效果估计往往会因违反稳定的单位治疗值假设而产生偏差。对于影响卖家战略选择、影响买家对其考虑因素的偏好或完全改变买家考虑因素的实验来说，这可能会特别成问题。在这项工作中，我们通过使用观察数据创建相似列表的聚类，然后使用这些聚类进行聚类随机现场实验来测量和减少在线市场实验中由于干扰引起的偏差。通过对两个实验设计进行随机化的Meta实验：一个Bernoulli随机化，一个cluster随机化，我们提供了一个由干扰引起的偏差的下界。在两个元实验中，治疗卖家与控制卖家受到不同的平台费用政策的约束，导致买家的价格不同。通过对两个元实验臂进行联合分析，我们发现两个设计获得的总平均治疗效果估计之间存在很大的统计显著性差异，并估计伯努利随机治疗效果估计的32.60%是由于干扰偏差造成的。我们还发现，干扰偏差的大小和/或方向取决于市场供应或需求受限程度的微弱证据，并分析了第二个元实验，以强调当治疗干预需要意图治疗分析时，检测干扰偏差的困难。
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英文标题：
《Reducing Interference Bias in Online Marketplace Pricing Experiments》
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作者：
David Holtz, Ruben Lobel, Inessa Liskovich, Sinan Aral
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最新提交年份：
2020
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分类信息：
一级分类：Statistics 统计学
二级分类：Methodology 方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Statistics 统计学
二级分类：Applications 应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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英文摘要：
Online marketplace designers frequently run A/B tests to measure the impact of proposed product changes. However, given that marketplaces are inherently connected, total average treatment effect estimates obtained through Bernoulli randomized experiments are often biased due to violations of the stable unit treatment value assumption. This can be particularly problematic for experiments that impact sellers' strategic choices, affect buyers' preferences over items in their consideration set, or change buyers' consideration sets altogether. In this work, we measure and reduce bias due to interference in online marketplace experiments by using observational data to create clusters of similar listings, and then using those clusters to conduct cluster-randomized field experiments. We provide a lower bound on the magnitude of bias due to interference by conducting a meta-experiment that randomizes over two experiment designs: one Bernoulli randomized, one cluster randomized. In both meta-experiment arms, treatment sellers are subject to a different platform fee policy than control sellers, resulting in different prices for buyers. By conducting a joint analysis of the two meta-experiment arms, we find a large and statistically significant difference between the total average treatment effect estimates obtained with the two designs, and estimate that 32.60% of the Bernoulli-randomized treatment effect estimate is due to interference bias. We also find weak evidence that the magnitude and/or direction of interference bias depends on extent to which a marketplace is supply- or demand-constrained, and analyze a second meta-experiment to highlight the difficulty of detecting interference bias when treatment interventions require intention-to-treat analysis.
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PDF链接：
https://arxiv.org/pdf/2004.12489
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