Research for Business, Government, and Society

During a global pandemic, how can we best prompt social media users to demonstrate discernment in sharing information online? We ran a contextual adaptive experiment on Facebook Messenger with users in Kenya and Nigeria and tested 40 combinations…

Public health organizations increasingly use social media advertising campaigns in pursuit of public health goals. In this paper, we evaluate the impact of about $40 million of social media advertisements that were run and experimentally tested…

We review economic arguments for using public policy to accelerate vaccine supply during a pandemic. Rapidly vaccinating a large share of the global population helps avoid economic, mortality, and social losses, which in the case of Covid-19…

We describe the design, implementation, and evaluation of a low-cost and scalable program that supports women in Poland in transitioning into jobs in the information technology sector. This program, called “Challenges,” helps…

In summer 2022, Stanford Graduate School of Business, the Hoover Institution Working Group on Corporate Governance at Stanford University, and Rock Center for Corporate Governance at Stanford University jointly conducted a nationwide survey of 2,…

Two leading hypotheses for why individuals unintentionally share misinformation are that 1) they are unable to recognize that a post contains misinformation, and 2) they make impulsive, emotional sharing decisions without thinking about whether a…

The operation of markets and of politics are in practice deeply intertwined. Political decisions set the rules of the game for market competition and, conversely, market competitors participate in and influence political decisions. We develop an…

The article offers information about the platform annexation, and the logic using basic principles from platform economics. It analyzes the platform annexation to the traditional antitrust categories in the market. It mentions that a platform…

Learning optimal policies from historical data enables the gains from personalization to be realized in a wide variety of applications. The growing policy learning literature focuses on a setting where the treatment assignment policy does not…

Online platforms often face challenges being both fair (i.e., non-discriminatory) and efficient (i.e., maximizing revenue). Using computer vision algorithms and observational data from a microlending marketplace, we find that choices made by…

Multi-armed bandit (MAB) algorithms are efficient approaches to reduce the opportunity cost of online experimentation and are used by companies to find the best product from periodically refreshed product catalogs. However, these algorithms face…

This report describes insights gleaned from the Data Fellows collaboration among PayPal, Northwestern University’s Kellogg School of Management, the Golub Capital Social Impact Lab at Stanford University’s Graduate School of Business, and…

As online platforms have become dominant, many have leveraged their power by raising fees and changing rules. In the short run, this hurts the producers they work with — software developers, small retailers, game designers, content creators. In…

Recent growing adoption of experimentation in practice has led to a surge of attention to multiarmed bandits as a technique to reduce the opportunity cost of online experiments. In this setting, a decision-maker sequentially chooses among a set…

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…