Browse or search publications from faculty affiliated with the lab.
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…
Learning optimal policies from historical data enables the gains from personalization to be realized in a wide variety of applications. The…
In this paper we study estimation of and inference for average treatment effects in a setting with panel data. We focus on the setting where units…
Adaptive experiment designs can dramatically improve statistical efficiency in randomized trials, but they also complicate statistical inference.…
Tractable contextual bandit algorithms often rely on the realizability assumption — i.e., that the true expected reward model belongs to a known…
Effective therapies for coronavirus disease 2019 (COVID-19) are urgently needed, and pre-clinical data suggest alpha-1 adrenergic receptor…
When researchers develop new econometric methods it is common practice to compare the performance of the new methods to those of existing methods…
This report describes insights gleaned from the Data Fellows collaboration among PayPal, Northwestern University’s Kellogg School of Management,…
Adaptive experiments present a unique opportunity to more rapidly learn which of many treatments work best, evaluate multiple hypotheses, and…