In regression discontinuity (RD) designs for evaluating causal effects of interventions, assignment
to a treatment is determined at least partly by the value of an observed covariate lying …

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 areas…

We outline a framework for causal inference in settings where assignment to a binary
treatment is ignorable, but compliance with the assignment is not perfect so that the receipt of …

Recently there has been a surge in econometric work focusing on estimating average
treatment effects under various sets of assumptions. One strand of this literature has developed …

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 …

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 …

This paper provides empirical evidence about the effect of unearned income on earnings,
consumption, and savings. Using an original survey of people playing the lottery in …

Estimation of average treatment effects in observational studies often requires adjustment
for differences in pre-treatment variables. If the number of pre-treatment variables is large, …

… simple matching estimator studied in this article (Abadie and Imbens (2005)). Software for
… Second, as we show in Abadie and Imbens (2002), under some additional smoothness con…

We are interested in estimating the average effect of a binary treatment on a scalar outcome.
If assignment to the treatment is exogenous or unconfounded, that is, independent of the …