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 …

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 …

Carbon offsets from voluntary avoided-deforestation projects are generated on the basis of
performance in relation to ex ante deforestation baselines. We examined the effects of 26 …

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

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 …

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

Synthetic control methods are commonly applied in empirical research to estimate the
effects of treatments or interventions on aggregate outcomes. A synthetic control estimator …

Background Mandatory COVID-19 certification (showing vaccination, recent negative test, or
proof of recovery) has been introduced in some countries. We aimed to investigate the effect …

We present a new estimator for causal effects with panel data that builds on insights behind
the widely used difference-in-differences and synthetic control methods. Relative to these …

Linear panel models, and the “event-study plots” that often accompany them, are popular
tools for learning about policy effects. We discuss the construction of event-study plots and …