The US spends more than 4 trillion dollars per year on health care, largely conducted by private providers and reimbursed by insurers. A major concern in this system is overbilling, waste and fraud by providers, who face incentives to misreport on their claims in order to receive higher...

Using only 34 published tables, we reconstruct five variables (census block, sex, age, race, and ethnicity) in the confidential 2010 Census person records. Using the 38-bin age variable tabulated at the census block level, at most 20.1% of reconstructed records can differ from their confidential...

Understanding factors affecting the direction of innovation is a central aim of research in the economics of innovation. Progress on this topic has been inhibited by difficulties in measuring distance and movement in knowledge space. We describe a methodology that infers the mapping of the...

Remotely sensed measurements and other machine learning predictions are increasingly used in place of direct observations in empirical analyses. Errors in such measures may bias parameter estimation, but it remains unclear how large such biases are or how to correct for them. We leverage a new...

We investigate the potential for Large Language Models (LLMs) to enhance scientific practice within experimentation by identifying key areas, directions, and implications. First, we discuss how these models can improve experimental design, including improving the elicitation wording, coding...

Economic policies often involve dynamic interventions, where individuals receive repeated interventions over multiple periods. This dynamics makes past responses informative to predict future responses and ultimate outcomes depend on the history of interventions. Despite these phenomena,...

While Artificial Intelligence (AI) algorithms have achieved performance levels comparable to human experts on various predictive tasks, human experts can still access valuable contextual information not yet incorporated into AI predictions. Humans assisted by AI predictions could outperform both...

We provide semiparametric identification results for a broad class of learning models in which continuous outcomes depend on three types of unobservables: i) known heterogeneity, ii) initially unknown heterogeneity that may be revealed over time, and iii) transitory uncertainty. We consider a...

This paper proposes a method to estimate treatment effects in difference-in-differences designs in which the treatment start is staggered over time and treatment effects are heterogeneous by group, time, and covariates, and when the data are repeated cross-sections. We show that a...

What do machines learn, and why? To answer these questions we import models of human cognition into machine learning. We propose two ways of modeling machine learners based on this join: feasibility-based and cost-based machine learning. We evaluate and estimate our models using a deep learning...