Missing data, especially when coupled with noncompliance, are a challenge even in the setting of randomized experiments. Although some existing methods can address each complication, it can be difficult to handle both of them simultaneously. This is true in the example of the New York City...

This survey gives a brief overview of the literature on the difference-in-difference (DiD) estimation strategy and discusses major issues using a treatment effect perspective. In this sense, this survey gives a somewhat different view on DiD than the standard textbook discussion of the...

Definitions of direct and indirect effects are given for settings in which individuals are clustered in groups or neighborhoods and in which treatments are administered at the group level. A particular intervention may affect individual outcomes both through its effect on the individual and by...

In this article the authors develop goodness-of-fit tests for fuzzy-set analyses to formally assess the fit between empirical information and various causal hypotheses while accounting for measurement error in membership scores. These goodness-of-fit tests, and the accompanying logic, provide a...

In observational studies, the estimation of a treatment effect on an outcome of interest is often done by controlling on a set of pre-treatment characteristics (covariates). This yields an unbiased estimator of the treatment effect when the assumption of unconfoundedness holds, that is, there...

The causal effect of a treatment on an outcome is generally mediated by several intermediate variables. Estimation of the component of the causal effect of a treatment that is not mediated by an intermediate variable (the direct effect of the treatment) is often relevant to mechanistic...