Economics

There are different reasons why experimenters may want to randomize their experiment at a region level. In some cases, treatments cannot be turned on or off at the individual level, therefore requiring randomization at a group level, for which regions can be a good candidate. In other cases, experimenters may worry about network effects or other types of spillovers within a geographic area, and opt to randomize

Online sites typically evaluate the impact of new product features on customer behavior using online controlled experiments (or A/B tests). For many business applications, it is important to detect heterogeneity in these experiments [1], as new features often have a differential impact by customer segment, product group, and other variables. Understanding heterogeneity can provide key insights into causal

Many data-driven companies measure the impact of product groups and allocate resources across them based on the estimated impacts of features they launch via A/B tests. In this doc, we show that, when based on a standard frequentist estimator of the impact of features, this practice can significantly overstate the impact of product groups and distort the allocation of resources. When this practice is instead

Labor market information is an important input to labor, workforce, education, and macroeconomic policy. However, granular and real-time data on labor market trends are lacking; publicly available data from survey samples are released with significant lags and miss critical information such as skills and benefits. We use generative Artificial Intelligence to automatically extract structured labor market

With recent rapid growth in online shopping, AI-powered Engagement Surfaces (ES) have become ubiquitous across retail services. These engagement surfaces perform an increasing range of functions, including recommending new products for purchase, remind-ing customers of their orders and providing delivery notifications. Understanding the causal effect of engagement surfaces on value driven for customers