  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.
