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 …

The difference in difference (DID) design is a quasi-experimental research design that
researchers often use to study causal relationships in public health settings where …

The Effect: An Introduction to Research Design and Causality is about research design,
specifically concerning research that uses observational data to make a causal inference. It …

Importance Many medical journals, includingJAMA, restrict the use of causal language to the
reporting of randomized clinical trials. Although well-conducted randomized clinical trials …

Probably because of their interpretability and transparent nature, synthetic controls have
become widely applied in empirical research in economics and the social sciences. This …

Cover crops are gaining traction in many agricultural regions, partly driven by increased
public subsidies and by private markets for ecosystem services. These payments are …

Causal effects of biodiversity on ecosystem functions can be estimated using experimental
or observational designs—designs that pose a tradeoff between drawing credible causal …

Explainable machine learning offers the potential to provide stakeholders with insights into
model behavior by using various methods such as feature importance scores, counterfactual …

Recent advances in artificial intelligence are primarily driven by machine learning, a
prediction technology. Prediction is useful because it is an input into decision-making. In …

Populism at the country level is at an all-time high, with more than 25 percent of nations
currently governed by populists. How do economies perform under populist leaders? We …