Formal analysis plans limit false discoveries by registering and multiplicity adjusting statistical tests. As each registered test reduces power on other tests, researchers prune hypotheses based on prior knowledge, often by combining related indicators into evenly-weighted indices. We propose...

We study the problem of estimating the average causal effect of treating every member of a population, as opposed to none, using an experiment that treats only some. We consider settings where spillovers have global support and decay slowly with (a generalized notion of) distance. We derive the...

Field experiments provide the clearest window into the true impact of many policies, allowing us to understand what works, what does not, and why. Yet, their widespread use has not been accompanied by a deep understanding of the political economy of their adoption in policy circles. This study...

We provide twelve best practices and discuss how each practice can help researchers accurately, credibly, and ethically use Generative AI (GenAI) to enhance experimental research. We split the twelve practices into four areas. First, in the pre-treatment stage, we discuss how GenAI can aid in...

We use a longitudinal dataset measuring beliefs and behaviors to study the dynamics of model - or narrative - adoption during the Covid-19 pandemic. We show that individuals switch beliefs about the effectiveness of preventive behaviors following changes in perceived risk. The adoption of...

This paper examines the scalability of the results from the Tennessee Student-Teacher Achievement Ratio (STAR) Project, a prominent educational experiment. We explore how the misalignment between the experimental design and the econometric model affects researchers' ability to learn about the...