Causal analysis of Covid-19 spread in Germany

TL;DR

This paper introduces a new causal feature selection method for time series data, applied to Covid-19 spread in Germany, revealing the impact of restriction policies despite limited data.

Contribution

We propose a novel theorem for causal feature selection in time series, robust to confounders, and demonstrate its application to Covid-19 case data in Germany.

Findings

Restriction measures have a causal impact on virus spread.

Limited data still contains detectable causal signals.

Method shows promise for data-driven policy analysis.

Abstract

In this work, we study the causal relations among German regions in terms of the spread of Covid-19 since the beginning of the pandemic, taking into account the restriction policies that were applied by the different federal states. We propose and prove a new theorem for a causal feature selection method for time series data, robust to latent confounders, which we subsequently apply on Covid-19 case numbers. We present findings about the spread of the virus in Germany and the causal impact of restriction measures, discussing the role of various policies in containing the spread. Since our results are based on rather limited target time series (only the numbers of reported cases), care should be exercised in interpreting them. However, it is encouraging that already such limited data seems to contain causal signals. This suggests that as more data becomes available, our causal approach may contribute towards meaningful causal analysis of political interventions on the development of Covid-19, and thus also towards the development of rational and data-driven methodologies for choosing interventions.