Economics

Causal Impact (CI) measurement is broadly used across the industry to inform both short- and long-term investment decisions of various types. In this paper, we apply the double machine learning (DML) methodology to estimate average and conditional average treatment effects across 100s of customer action types for e-commerce and digital businesses and 100s of millions of customers that can be used in decisions

The majority of students who choose to major in engineering do so to become a part of the community of practice of professional engineers (Johri & Olds, 2011), meaning that they want to have adequate exposure to what a career as a professional engineer could potentially be as part of their college experience. However, according to Jonassen (2014), engineering graduates are not well trained to contribute

Network interference, where observed outcomes are influenced by interaction with nearby units, is a fundamental issue in A/B testing and experimentation in social and economic networks. Clustered randomization is a frequently-used strategy that aims to prevent confounding by limiting interaction between treated and untreated units. We study a model of least-squares estimation under network interference,

Computer science (CS) is special among STEM subjects: it aims at an industry sector that has the most job growth but has a constant shortage in the workforce; it is a relatively young and burgeoning subject in K-12 education that has a shortage of classroom teachers; and it is one of a very few STEM subjects that large number of students can master by learning it completely out-of-school. To inspire the

Classical Randomized Controlled Trials (RCTs), or A/B tests, are designed to draw causal inferences about a population of units, for example, individuals, plots of land or visits to a website. A key assumption underlying a standard RCT is the absence of interactions between units, or the stable unit treatment value assumption (Ann. Statist. 6 (1978) 34-58). Modem experimentation, however, is often conducted