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 …

Over the last several decades, voluntary certification programs have become a key
approach to promote sustainable supply chains for agricultural commodities. These …

This paper synthesizes recent advances in the econometrics of difference-in-differences
(DiD) and provides concrete recommendations for practitioners. We begin by articulating a …

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

This article proposes doubly robust estimators for the average treatment effect on the treated
(ATT) in difference-in-differences (DID) research designs. In contrast to alternative DID …

Shelter-in-place ordinances were the first wide-spread policy measures aimed to mitigate
the spread of COVID-19. Compliance with shelter-in-place directives is individually costly …

In this paper we study estimation of and inference for average treatment effects in a setting
with panel data. We focus on the staggered adoption setting where units, eg, individuals …

This paper discusses two important limitations of the common practice of testing for
preexisting differences in trends (“pre-trends”) when using difference-in-differences and …

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

In this survey we discuss the recent causal panel data literature. This recent literature has
focused on credibly estimating causal effects of binary interventions in settings with …