# Synthetic Controls in Action

arXiv: https://arxiv.org/abs/2203.06279 · HTML: https://mlsanity.com/paper/2203.06279/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2022.

## Built on by (most cited first, 3 of 3)

- [Empirical evidence of Large Language Model's influence on human spoken communication](https://mlsanity.com/paper/2409.01754/) (2024)
- [Predictor Selection for Synthetic Controls](https://mlsanity.com/paper/2203.11576/) (2022)
- [Validation of massively-parallel adaptive testing using dynamic control matching](https://mlsanity.com/paper/2305.01334/) (2023)

## Abstract (arXiv)

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.