Balancing, Regression, Difference-In-Differences and Synthetic Control Methods: A Synthesis

TL;DR

This paper generalizes synthetic control methods by allowing negative weights and permanent differences, integrating ideas from difference-in-differences to improve treatment effect estimation with multiple control units.

Contribution

It introduces a flexible framework combining synthetic control and difference-in-differences, accommodating more complex treatment effect scenarios.

Findings

Enhanced estimation accuracy in simulations.

Robustness to model misspecification.

Applicability to diverse treatment settings.

Abstract

In a seminal paper Abadie, Diamond, and Hainmueller [2010] (ADH), see also Abadie and Gardeazabal [2003], Abadie et al. [2014], develop the synthetic control procedure for estimating the effect of a treatment, in the presence of a single treated unit and a number of control units, with pre-treatment outcomes observed for all units. The method constructs a set of weights such that selected covariates and pre-treatment outcomes of the treated unit are approximately matched by a weighted average of control units (the synthetic control). The weights are restricted to be nonnegative and sum to one, which is important because it allows the procedure to obtain unique weights even when the number of lagged outcomes is modest relative to the number of control units, a common setting in applications. In the current paper we propose a generalization that allows the weights to be negative, and their sum to differ from one, and that allows for a permanent additive difference between the treated unit and the controls, similar to difference-in-difference procedures. The weights directly minimize the distance between the lagged outcomes for the treated and the control units, using regularization methods to deal with a potentially large number of possible control units.