Publications by Golub Capital Social Impact Lab

In medicine, randomized clinical trials are the gold standard for informing treatment decisions. Observational comparative effectiveness research is often plagued by selection bias, and expert-selected covariates may not be sufficient to adjust…

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…

In many areas, practitioners seek to use observational data to learn a treatment assignment policy that satisfies application-specific constraints, such as budget, fairness, simplicity, or other functional form constraints. For example,…

Computationally efficient contextual bandits are often based on estimating a predictive model of rewards given contexts and arms using past data. However, when the reward model is not well-specified, the bandit algorithm may incur unexpected…

Random forests are a powerful method for non-parametric regression, but are limited in their ability to fit smooth signals. Taking the perspective of random forests as an adaptive kernel method, we pair the forest kernel with a local linear…

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…

Tractable contextual bandit algorithms often rely on the realizability assumption — i.e., that the true expected reward model belongs to a known class, such as linear functions. We investigate issues that arise in the absence of realizability and…

Alongside the outbreak of the novel coronavirus, an “infodemic” of myths and hoax cures is spreading over online media outlets and social media platforms. Building on the literature on combating fake news, we evaluate experimental interventions…

In the urgent setting of the COVID-19 pandemic, treatment hypotheses abound, each of which requires careful evaluation. A randomized controlled trial generally provides the strongest possible evaluation of a treatment, but the efficiency and…

In severe viral pneumonia, including Coronavirus disease 2019 (COVID-19), the viral replication phase is often followed by hyperinflammation (‘cytokine storm syndrome’), which can lead to acute respiratory distress syndrome, multi-organ failure,…

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 problem of learning treatment assignment policies from randomized or observational data arises in many fields. For example, in personalized medicine, we seek to map patient observables (like age, gender, heart pressure, etc.) to a treatment…

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…

For many machine learning algorithms, two main assumptions are required to guarantee performance. One is that the test data are drawn from the same distribution as the training data, and the other is that the model is correctly specified. In real…

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…

We consider a variant of the contextual bandit problem. In standard contextual bandits, when a user arrives we get the user’s complete feature vector and then assign a treatment (arm) to that user. In a number of applications (like health…

Adaptive experiment designs can dramatically improve statistical efficiency in randomized trials, but they also complicate statistical inference. For example, it is now well known that the sample mean is biased in adaptive trials. Inferential…

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

Consider a researcher estimating the parameters of a regression function based on data for all 50 states in the United States or on data for all visits to a website. What is the interpretation of the estimated parameters and the standard errors?…

For many machine learning algorithms, two main assumptions are required to guarantee performance. One is that the test data are drawn from the same distribution as the training data, and the other is that the model is correctly specified. In real…

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…

A common challenge in estimating the long-term impacts of treatments (e.g., job training programs) is that the outcomes of interest (e.g., lifetime earnings) are observed with a long delay. We address this problem by combining several short-term…