Socioeconomic disparities and COVID-19: the causal connections

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Tác giả: Tannista Banerjee, Ayan Paul, Vishak Srikanth, Inga Strümke

Ngôn ngữ: eng

Ký hiệu phân loại: 339.22 Distribution of aggregate personal income and wealth

Thông tin xuất bản: 2022

Mô tả vật lý:

Bộ sưu tập: Metadata

ID: 168589

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.
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