Complications in applied work often prevent researchers from obtaining unique point estimates of target quantities using cheaply available data—at best, ranges of possibilities, or sharp bounds, can be reported. To make progress, researchers frequently...
We study how people change their behavior after learning they are biased. Teachers in Italian schools give lower grades to immigrant students relative to natives with comparable ability. In two experiments, we reveal to teachers their own bias, measured...
Over the last few decades, social scientists have developed and applied a host of statistical methods to make valid causal inferences, known as the credibility revolution. This trend has primarily focused on internal validity — researchers aim to...
We describe a new design-based framework for drawing causal inference in randomized experiments. Causal effects in the framework are defined as linear functionals evaluated at potential outcome functions. Knowledge and assumptions about the potential...
Target trials are RCTs one would like to conduct but cannot for ethical, financial, and/or logistical reasons. Consequently, we must emulate such trials from observational data. A novel aspect of target trial methodology is that, for purposes of data...
The estimation of racial disparities in health care, financial services, voting, and other contexts is often hampered by the lack of individual-level racial information in administrative records. In many cases, the law prohibits the collection of such...
Throughout the COVID-19 pandemic, policymakers have proposed risk metrics, such as the CDC Community Levels, to guide local and state decision-making. However, risk metrics have not reliably predicted key outcomes and often lack transparency in terms of...
When running multi-arm trials, experimenters may wish to both learn and evaluate data-driven policies; for example, learning which version of treatment is most effective and evaluating the effect of that treatment in comparison to a control condition...
Many organizations run thousands of randomized experiments, or A/B tests, to statistically quantify and detect the impact of product changes. Analysts take these results to augment decision-making around deployment and investment opportunities, making...
The regression discontinuity (RD) design is widely used for program evaluation with observational data. The RD design enables the identification of the local average treatment effect (LATE) at the treatment cutoff by exploiting known deterministic...
