Browse or search publications from faculty affiliated with the lab.
Contextual bandit algorithms often estimate reward models to inform decision-making. However, true rewards can contain action-independent…
During a global pandemic, how can we best prompt social media users to demonstrate discernment in sharing information online? We ran a contextual…
We describe the design, implementation, and evaluation of a low-cost and scalable program that supports women in Poland in transitioning into jobs…
Two leading hypotheses for why individuals unintentionally share misinformation are that 1) they are unable to recognize that a post contains…
Learning optimal policies from historical data enables the gains from personalization to be realized in a wide variety of applications. The…
Online platforms often face challenges being both fair (i.e., non-discriminatory) and efficient (i.e., maximizing revenue). Using computer vision…
Labor economists regularly analyze employment data by fitting predictive models to small, carefully constructed longitudinal survey datasets.…
This report describes insights gleaned from the Data Fellows collaboration among PayPal, Northwestern University’s Kellogg School of Management,…
Randomized clinical trials (RCT) are the gold standard for informing treatment decisions. Observational studies are often plagued by selection…
Causal inference has recently attracted substantial attention in the machine learning and artificial intelligence community. It is usually…
We estimate a measure of segregation, experienced isolation, that captures individuals’ exposure to diverse others in the places they visit over…
We develop new semiparametric methods for estimating treatment effects. We focus on a setting where the outcome distributions may be thick tailed…
Long-acting reversible contraceptives are highly effective in preventing unintended pregnancies, but take-up remains low. This paper analyzes a…
It has become increasingly common for data to be collected adaptively, for example using contextual bandits. Historical data of this type can be…
Analyzing observational data from multiple sources can be useful for increasing statistical power to detect a treatment effect; however, practical…
Since the beginning of the COVID-19 pandemic, pharmaceutical treatment hypotheses have abounded, each requiring careful evaluation. A randomized…
Breiman’s “Two Cultures” paper painted a picture of two disciplines, data modeling, and algorithmic machine learning, both engaged in the analyses…
Computational social science is more than just large repositories of digital data and the computational methods needed to construct and analyze…
In severe viral pneumonia, including Coronavirus disease 2019 (COVID-19), the viral replication phase is often followed by hyperinflammation,…
We study the problem of model selection for contextual bandits, in which the algorithm must balance the bias-variance trade-off for model…