We discuss the relevance of the recent machine learning (ML) literature for economics and
econometrics. First we discuss the differences in goals, methods, and settings between the …

Program evaluation methods are widely applied in economics to assess the effects of policy
interventions and other treatments of interest. In this article, we describe the main …

Media outlets often present diverging, even conflicting, perspectives on reality—not only
informing, but potentially misinforming audiences. We study the extent to which …

The scientific community has come together in a mass mobilization to combat the public
health risks of COVID-19, including efforts to develop a vaccine. However, the success of …

Clustered standard errors, with clusters defined by factors such as geography, are
widespread in empirical research in economics and many other disciplines. Formally …

For scientific discoveries to be valid—whether in theory or empirically—a phenomenon must
be accurately described: The scientist must use appropriate counterfactuals and eliminate …

In principle, experiments offer a straightforward method for social scientists to accurately
estimate causal effects. However, scholars often unwittingly distort treatment effect estimates …

Knowledge of causal effects is of great importance to decision makers in a wide variety of
settings. In many cases, however, these causal effects are not known to the decision makers …

The promise of randomized controlled trials is that evidence gathered through the evaluation
of a specific program helps us—possibly after several rounds of fine-tuning and multiple …

High-achieving, low-income students attend selective colleges at far lower rates than upper-
income students with similar achievement. Behavioral biases, intensified by complexity and …