With the rapid growth of omnichannel retailing, digitally native retailers are increasingly opening physical stores. A critical issue for many digitally native retailers is to estimate the causal effect of a new store opening on their online sales. To assess the causal effect, a randomized...

This article explores new methods for gathering and analyzing spatially rich demographic data using mobile phones. It describes a pilot study (the Human Mobility Project) in which volunteers around the world were successfully recruited to share GPS and cellular tower information on their...

In this paper we consider using a factor-model-based method, also known as the generalized synthetic control method, to estimate average treatment effects. This method is best suited for cases where there is only one (or a few) treated unit(s), a large number of control units, and large pre and...

The difference-in-differences (DID) method is the most widely used tool to answer causal questions from quasi-experimental data in marketing and the broader social sciences. Since assignment to treatment is often not random, the selection of proper control units is critically important for...

Marketing scientists often estimate causal effects using data from pre/post test/control quasi-experimental settings. We propose a new, easy to implement Augmented Difference-in-Differences (ADID) method that complements existing approaches to estimate the average treatment effect on the treated...