External validity captures the extent to which inferences drawn from a given study's sample
apply to a broader population or other target populations. Social scientists frequently invoke …

When assessing causal effects, determining the target population to which the results are
intended to generalize is a critical decision. Randomized and observational studies each …

The canonical difference-in-differences (DD) estimator contains two time periods,” pre” and”
post”, and two groups,” treatment” and” control”. Most DD applications, however, exploit …

Abstract Randomized Controlled Trials (RCTs) are increasingly popular in the social
sciences, not only in medicine. We argue that the lay public, and sometimes researchers, put …

In this paper, we discuss recent developments in econometrics that we view as important for
empirical researchers working on policy evaluation questions. We focus on three main …

In this chapter, we present econometric and statistical methods for analyzing randomized
experiments. For basic experiments, we stress randomization-based inference as opposed …

We review concepts, principles, and tools that unify current approaches to causal analysis
and attend to new challenges presented by big data. In particular, we address the problem …

Great care is taken in epidemiologic studies to ensure the internal validity of causal effect
estimates; however, external validity has received considerably less attention. When the …

The supplementary material contains details on treatment effect estimation performed
separately on RCT data (Section A) and on observational data (Section B), derivations of the …

Many empirical questions in economics and other social sciences depend on causal effects
of programs or policies. In the last two decades, much research has been done on the …