Synthetic Controls in Action

TL;DR

This paper presents guiding principles for empirical synthetic control studies, focusing on avoiding over-fitting, ensuring interpretability, and validating results through formal properties and visual demonstrations.

Contribution

It introduces a set of simple, formal principles to improve the practice and reliability of synthetic control methods in empirical research.

Findings

Principles help prevent over-fitting biases.

Visual demonstrations illustrate the principles' relevance.

Guidelines improve interpretability and validation of synthetic control results.

Abstract

In this article we propose a set of simple principles to guide empirical practice in synthetic control studies. The proposed principles follow from formal properties of synthetic control estimators, and pertain to the nature, implications, and prevention of over-fitting biases within a synthetic control framework, to the interpretability of the results, and to the availability of validation exercises. We discuss and visually demonstrate the relevance of the proposed principles under a variety of data configurations.