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

Granify是一家总部位于埃德蒙顿的公司，它使用人工智能、行为消息传递和预测分析来优化电子商务转化率和收入。在客户访问期间，Granify每秒监控各种属性，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确定特定客户的 ** 反对可能会阻止他们购买，它将自动引入一个消息或刺激来 ** 减轻他们的担忧并增加他们购买的机会。为了量化客户对Granify引入的 ** 消息或刺激的反应，我们需要估计Granify干预的治疗效果。标准的A/B测试通常是通过将客户会话随机分为两组进行的，有和 ** 没有Granify干预（分别为治疗组和对照组）。目前，平均治疗效果 **（ATE），即治疗组和对照组之间每次会话花费的平均金额之差，** 用于衡量实施Granify的效果。然而，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对治疗做出反应。在样本的这种平均效应的基础上，可能是特定客户对治疗的反应存在很大差异。这种变化可能揭示了Granify干预的效果如何取决于客户的特征，并且 ** 它有助于提供有关如何最佳管理治疗的指导。此外，ATE通常对结果分布的离群值和偏度敏感。本研究的目标是双重的：第一，测试和 ** 估计Granify干预的异质性治疗效果;第二，提供一个强大的位置 ** 估计和相应的置信区间的治疗效果。

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.