Browse or search publications from faculty affiliated with the lab.
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…
The problem of learning treatment assignment policies from randomized or observational data arises in many fields. For example, in personalized…
The allocation of decision authority by a principal to either a human agent or an artificial intelligence is examined. The principal trades off an…
For many machine learning algorithms, two main assumptions are required to guarantee performance. One is that the test data are drawn from the…
The rise of marketplaces for goods and services has led to changes in the mechanisms used to ensure high quality. We analyze this phenomenon in…
Many learning algorithms require categorical data to be transformed into real vectors before it can be used as input. Often, categorical…
Contextual bandit algorithms are sensitive to the estimation method of the outcome model as well as the exploration method used, particularly in…
This paper proposes a method for estimating consumer preferences among discrete choices, where the consumer chooses at most one product in a…