Browse or search publications from faculty affiliated with the lab.
Generic Drug Repurposing for Public Health and National Security: COVID-19 and Beyond
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…
Tractable Contextual Bandits Beyond Realizability
Tractable contextual bandit algorithms often rely on the realizability assumption — i.e., that the true expected reward model belongs to a known…
Optimal Policies to Battle the Coronavirus “Infodemic” Among Social Media Users in Sub-Saharan Africa: Pre-analysis Plan
Alongside the outbreak of the novel coronavirus, an “infodemic” of myths and hoax cures is spreading over online media outlets and social media…
A How-To Guide for Conducting Retrospective Analyses: Example COVID-19 Study
In the urgent setting of the COVID-19 pandemic, treatment hypotheses abound, each of which requires careful evaluation. A randomized controlled…
Alpha-1 Adrenergic Receptor Antagonists for Preventing Acute Respiratory Distress Syndrome and Death from Cytokine Storm Syndrome
In severe viral pneumonia, including Coronavirus disease 2019 (COVID-19), the viral replication phase is often followed by hyperinflammation (‘…
Combining Experimental and Observational Data to Estimate Treatment Effects on Long Term Outcomes
There has been an increase in interest in experimental evaluations to estimate causal effects, partly because their internal validity tends to be…
policytree: Policy Learning via Doubly Robust Empirical Welfare Maximization over Trees
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 to Human and Artificial Intelligence
The allocation of decision authority by a principal to either a human agent or an artificial intelligence is examined. The principal trades off an…
Stable Prediction with Model Misspecification and Agnostic Distribution Shift
For many machine learning algorithms, two main assumptions are required to guarantee performance. One is that the test data are drawn from the…
SHOPPER: A Probabilistic Model of Consumer Choice with Substitutes and Complements
We develop SHOPPER, a sequential probabilistic model of shopping data. SHOPPER uses interpretable components to model the forces that drive how a…
Survey Bandits with Regret Guarantees
We consider a variant of the contextual bandit problem. In standard contextual bandits, when a user arrives we get the user’s complete…
Confidence Intervals for Policy Evaluation in Adaptive Experiments
Adaptive experiment designs can dramatically improve statistical efficiency in randomized trials, but they also complicate statistical inference.…
The Allocation of Decision Authority to Human and Artificial Intelligence
The allocation of decision authority by a principal to either a human agent or an artificial intelligence (AI) is examined. The principal trades…
Stable Prediction with Model Misspecification and Agnostic Distribution Shift
For many machine learning algorithms, two main assumptions are required to guarantee performance. One is that the test data are drawn from the…
Economists (and Economics) in Tech Companies
As technology platforms have created new markets and new ways of acquiring information, economists have come to play an increasingly central role…
Service Quality in the Gig Economy: Empirical Evidence about Driving Quality at Uber
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…
The Surrogate Index: Combining Short-Term Proxies to Estimate Long-Term Treatment Effects More Rapidly and Precisely
A common challenge in estimating the long-term impacts of treatments (e.g., job training programs) is that the outcomes of interest (e.g.,…
Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations
Researchers often use artificial data to assess the performance of new econometric methods. In many cases the data generating processes used in…
Sufficient Representations for Categorical Variables
Many learning algorithms require categorical data to be transformed into real vectors before it can be used as input. Often, categorical…
Balanced Linear Contextual Bandits
Contextual bandit algorithms are sensitive to the estimation method of the outcome model as well as the exploration method used, particularly in…
Synthetic Difference in Differences
We present a new perspective on the Synthetic Control (SC) method as a weighted least squares regression estimator with time fixed effects…
Generalized Random Forests
We propose generalized random forests, a method for nonparametric statistical estimation based on random forests (Breiman [Mach. Learn. …
Estimation Considerations in Contextual Bandits
Contextual bandit algorithms are sensitive to the estimation method of the outcome model as well as the exploration method used,…
Offline Multi-Action Policy Learning: Generalization and Optimization
In many settings, a decision-maker wishes to learn a rule, or policy, that maps from observable characteristics of an individual to an action.…