Socioeconomic disparities and COVID-19: the causal connections

by   Tannista Banerjee, et al.

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.


page 1

page 17


Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models

Shapley values underlie one of the most popular model-agnostic methods w...

Estimating a Causal Order among Groups of Variables in Linear Models

The machine learning community has recently devoted much attention to th...

Causal Bias Quantification for Continuous Treatment

In this work we develop a novel characterization of marginal causal effe...

Explaining the data or explaining a model? Shapley values that uncover non-linear dependencies

Shapley values have become increasingly popular in the machine learning ...

Causal analysis of Covid-19 spread in Germany

In this work, we study the causal relations among German regions in term...

Using Non-Linear Causal Models to Study Aerosol-Cloud Interactions in the Southeast Pacific

Aerosol-cloud interactions include a myriad of effects that all begin wh...

A Causal Framework for Decomposing Spurious Variations

One of the fundamental challenges found throughout the data sciences is ...

Please sign up or login with your details

Forgot password? Click here to reset