Robust estimation of treatment effects in high-dimensional heterogenous data with application to e-commerce

高维异构数据处理效果的鲁棒估计及其在电子商务中的应用

• 批准号: 523105-2018
• 负责人: Kong, Linglong
• 金额: $ 1.82万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=655851
• 关键词: Robust; estimation; treatment; effects; dimensional

Granify is an Edmonton-based company that uses artificial intelligence, behavioral messaging and predictive**analytics to optimize e-commerce conversion rates and revenues. During a customer's visit, Granify monitors**various attributes per second such as scroll speed, products and images viewed, mouse movements, and**hesitations, to fully understand the customer's digital behavior. If Granify determines a specific customer's**objection is likely to prevent them from purchasing, it will automatically introduce a message or stimuli to**alleviate their concern and increase their chance of purchasing.To quantify how customers respond to the**message or stimuli introduced by Granify, we need to estimate the treatment effect of the Granify intervention.**A standard A/B test is usually conducted by randomly segmenting customer sessions into groups, both with and**without Granify intervention (treatment and control group, respectively).Currently, average treatment effect**(ATE), the difference of the average money spent per session between treatment group and control group, is**used to measure the effect of implementing Granify. However, ATE only provides a partial view over the**effects of the treatment since each customer might respond to the treatment in different ways. Underlying this**average effect for a sample may be substantial variation in how particular customers respond to treatments.**This variation may reveal how the effect of the Granify intervention depends on customers' characteristics, and**it is useful to provide guidance on how to optimally administer treatments. Furthermore, ATE is often sensitive**to outliers and skewness of the outcome distribution. The goal of this research is twofold: first, to test and**estimate heterogeneous treatment effects of Granify intervention; and second, to provide a robust location**estimator and corresponding confidence interval for the treatment effect.

Granify是一家位于埃德蒙顿的公司，利用人工智能、行为信息和预测分析来优化电子商务的转化率和收入。在客户访问期间，Granify每秒监测各种属性，如滚动速度、查看的产品和图像、鼠标移动和犹豫，以充分了解客户的数字行为。如果Granify确定一个特定的客户的**反对可能会阻止他们购买，它会自动引入一个消息或刺激，以减轻他们的担忧，增加他们的购买机会。为了量化客户对Granify引入的**信息或刺激的反应，我们需要估计Granify干预的治疗效果。**标准的A/B测试通常是通过随机将客户会话分成有和没有Granify干预的组（分别是实验组和对照组）来进行的。目前，平均治疗效果**(ATE)，即治疗组和对照组每次平均花费的钱的差异，被**用来衡量实施Granify的效果。但是，ATE只提供了治疗效果的部分视图，因为每个客户可能以不同的方式响应治疗。在样本平均效应的基础上，可能是特定客户对治疗的反应存在实质性差异。**这种变化可能揭示Granify干预的效果如何取决于客户的特征，并且**对于如何最佳地管理治疗提供指导是有用的。此外，ATE通常对结果分布的异常值和偏度很敏感。本研究的目的有两个：第一，测试和评估Granify干预的异质性治疗效果；第二，为处理效果提供鲁棒的位置估计量和相应的置信区间。