Socioeconomic disparities and COVID-19: the causal connections

TL;DR

This paper introduces causal Shapley values, a method combining cooperative game theory and do calculus, to analyze socioeconomic disparities' causal links to COVID-19 spread, highlighting the advantages of non-linear models.

Contribution

It proposes a novel causal explanation framework using causal Shapley values for analyzing socioeconomic factors affecting COVID-19, integrating machine learning and causal inference.

Findings

Causal Shapley values reveal changing causal links over time.

Non-linear models outperform linear models in multivariate causal analysis.

Causal structure enhances variable importance interpretation.

Abstract

The analysis of causation is a challenging task that can be approached in various ways. With the increasing use of machine learning based models in computational socioeconomics, explaining these models while taking causal connections into account is a necessity. In this work, we advocate the use of an explanatory framework from cooperative game theory augmented with $do$ calculus, namely causal Shapley values. Using causal Shapley values, we analyze socioeconomic disparities that have a causal link to the spread of COVID-19 in the USA. We study several phases of the disease spread to show how the causal connections change over time. We perform a causal analysis using random effects models and discuss the correspondence between the two methods to verify our results. We show the distinct advantages a non-linear machine learning models have over linear models when performing a multivariate analysis, especially since the machine learning models can map out non-linear correlations in the data. In addition, the causal Shapley values allow for including the causal structure in the variable importance computed for the machine learning model.