Research for Business, Government, and Society

With rising access to electronic health record data, application of artificial intelligence to create clinical risk prediction models has grown. A key component in designing these models is feature generation. Methods used to generate features…

We study a Bayesian k-armed bandit problem in many-armed regime, when k ≥ √ T, with T the time horizon. We first show that subsampling is critical for designing optimal policies. Specifically, the standard UCB…

Randomized clinical trials (RCT) are the gold standard for informing treatment decisions. Observational studies are often plagued by selection bias, and expert-selected covariates may insufficiently adjust for confounding. We explore how…

Voluntary shelter-in-place directives and lockdowns are the main non-pharmaceutical interventions that governments around the globe have used to contain the Covid-19 pandemic. In this paper we study the impact of such interventions in the capital…

This paper proposes a method for estimating consumer preferences among discrete choices, where the consumer chooses at most one product in a category, but selects from multiple categories in parallel. The consumer’s utility is additive in the…

We estimate a measure of segregation, experienced isolation, that captures individuals’ exposure to diverse others in the places they visit over the course of their days. Using Global Positioning System (GPS) data collected from smartphones, we…

Long-acting reversible contraceptives are highly effective in preventing unintended pregnancies, but take-up remains low. This paper analyzes a randomized controlled trial of interventions addressing two barriers to long-acting reversible…

Since the beginning of the COVID-19 pandemic, pharmaceutical treatment hypotheses have abounded, each requiring careful evaluation. A randomized controlled trial generally provides the most credible evaluation of a treatment, but the efficiency…

Computational social science is more than just large repositories of digital data and the computational methods needed to construct and analyze them. It also represents a convergence of different fields with different ways of thinking about and…

Part of thesis finalist of 2015 INFORMS George Dantzig Dissertation Award. Second place 2015 M&SOM Student Paper Competition.

We consider the problem faced by a procurement agency that runs a mechanism for constructing an…

Effective therapies for coronavirus disease 2019 (COVID-19) are urgently needed, and pre-clinical data suggest alpha-1 adrenergic receptor antagonists (α1-AR antagonists) may be effective in reducing mortality related to hyperinflammation…

Emergency Department (ED) crowding is a major public health challenge since it can seriously impact patient outcomes; and accurate prediction of patient flow in EDs is essential for improving operational efficiency and quality of care. We present…

Adaptive experiments present a unique opportunity to more rapidly learn which of many treatments work best, evaluate multiple hypotheses, and optimize for several objectives. For example, they can be used to pilot a large number of potential…

Each month, COVID-19 kills hundreds of thousands of people, reduces global gross domestic product (GDP) by hundreds of billions of dollars, and generates large, accumulating losses to human capital by harming education and health (…

Alpha 1–adrenergic receptor blocking agents (α1-blockers) have been reported to have protective benefits against hyperinflammation and cytokine storm syndrome, conditions that are associated with mortality in patients with coronavirus disease…

Vaccinating the world’s population quickly in a pandemic has enormous health and economic benefits. We analyze the problem faced by governments in determining the scale and structure of procurement for vaccines. We analyze alternative approaches…

The novel disease caused by the SARS-CoV-2 virus (COVID-19) has been a shock to both our health and wealth, with more than 276,000 dead in the U.S. and economic disruption that some have estimated as high as more than $16 trillion. These…

There has been an increase in interest in experimental evaluations to estimate causal effects, partly because their internal validity tends to be high. At the same time, as part of the big data revolution, large, detailed, and representative,…

The allocation of decision authority by a principal to either a human agent or an artificial intelligence is examined. The principal trades off an AI’s more aligned choice with the need to motivate the human agent to expend effort in learning…

We develop SHOPPER, a sequential probabilistic model of shopping data. SHOPPER uses interpretable components to model the forces that drive how a customer chooses products; in particular, we designed SHOPPER to capture how items interact with…

Big data have enabled decision makers to tailor decisions at the individual level in a variety of domains, such as personalized medicine and online advertising. Doing so involves learning a model of decision rewards conditional on individual-…

As technology platforms have created new markets and new ways of acquiring information, economists have come to play an increasingly central role in tech companies-tackling problems such as platform design, strategy, pricing, and policy. Over the…

In many prediction problems researchers have found that combinations of prediction methods (“ensembles”) perform better than individual methods. In this paper we apply these ideas to synthetic control type problems in panel data. Here a number of…

Recent Medicare legislation has been directed at improving patient care quality by financially penalizing providers for hospital-acquired infections (HAIs). However, Medicare cannot directly monitor HAI rates, and instead relies on providers…