This essay describes the evolution and recent convergence of two methodological
approaches to causal inference. The first one, in statistics, started with the analysis and …

This article studies experimental design in settings where the experimental units are large
aggregate entities (eg, markets), and only one or a small number of units can be exposed to …

Principal component regression (PCR) is a popular technique for fixed-design error-in-
variables regression, a generalization of the linear regression setting in which the observed …

Since their introduction by Abadie and Gardeazabal, Synthetic Control (SC) methods have
quickly become one of the leading methods for estimating causal effects in observational …

We consider situations where a user feeds her attributes to a machine learning method that
tries to predict her best option based on a random sample of other users. The predictor is …

We study the estimation of treatment effects of a binary policy in environments with a
staggered treatment rollout. We propose a new estimator--Sequential Synthetic Difference in …

Static supervised learning—in which experimental data serves as a training sample for the
estimation of an optimal treatment assignment policy—is a commonly assumed framework of …

Synthetic control methods are a data-driven way to calculate counterfactuals from control
individuals for the estimation of treatment effects in many settings of empirical importance. In …

Excessive turnover can signicantly impair an organization's performance. Using high-quality
administrative data and staggered dierence-in-dierences strategies, we evaluate the impact …

The synthetic control (SC) method is popular for estimating causal effects from observational
panel data. It rests on a crucial assumption that we can write the treated unit as a linear …