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 develop new semiparametric methods for estimating treatment effects. We focus on a setting where the outcome distributions may be thick tailed…
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…
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…
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…
In medicine, randomized clinical trials are the gold standard for informing treatment decisions. Observational comparative effectiveness research…
Alpha 1–adrenergic receptor blocking agents (α1-blockers) have been reported to have protective benefits against hyperinflammation and cytokine…
In many areas, practitioners seek to use observational data to learn a treatment assignment policy that satisfies application-specific…
Random forests are a powerful method for non-parametric regression, but are limited in their ability to fit smooth signals. Taking the perspective…
In the urgent setting of the COVID-19 pandemic, treatment hypotheses abound, each of which requires careful evaluation. A randomized controlled…
In severe viral pneumonia, including Coronavirus disease 2019 (COVID-19), the viral replication phase is often followed by hyperinflammation (‘…
The novel disease caused by the SARS-CoV-2 virus (COVID-19) is both a shock to our health and our wealth, with more than 110,000 dead in the U.S…
There has been an increase in interest in experimental evaluations to estimate causal effects, partly because their internal validity tends to be…