# Synthetic Control, Synthetic Interventions, and COVID-19 spread: Exploring the impact of lockdown measures and herd immunity

arXiv: https://arxiv.org/abs/2009.09987 · HTML: https://mlsanity.com/paper/2009.09987/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2020.

## Built on by (most cited first, 2 of 2)

- [Heterogeneous Synthetic Learner for Panel Data](https://mlsanity.com/paper/2212.14580/) (2022)
- [Efficiently Learning Synthetic Control Models for High-dimensional Disaggregated Data](https://mlsanity.com/paper/2510.22828/) (2025)

## Abstract (arXiv)

The synthetic control method is an empirical methodology forcausal inference using observational data. By observing thespread of COVID-19 throughout the world, we analyze the dataon the number of deaths and cases in different regions usingthe power of prediction, counterfactual analysis, and syntheticinterventions of the synthetic control and its extensions. Weobserve that the number of deaths and cases in different re-gions would have been much smaller had the lockdowns beenimposed earlier and had the re-openings been done later, es-pecially among indoor bars and restaurants. We also analyzethe speculated impact of herd immunity on the spread giventhe population of each region and show that lockdown policieshave a very strong impact on the spread regardless of the levelof prior infections. Our most up-to-date code, model, and data can be foundon github: https://github.com/niloofarbayat/COVID19-synthetic-control-analysis