论文标题
社会经济差异和共同差异19：因果关系
Socioeconomic disparities and COVID-19: the causal connections
论文作者
论文摘要
因果关系的分析是一项具有挑战性的任务，可以通过各种方式处理。随着计算社会经济学中基于机器学习的模型的越来越多，在考虑因果关系的同时解释了这些模型是必要的。在这项工作中，我们主张使用合作游戏理论的解释框架使用$ do $ cyculus，即因果莎普利价值观。使用因果沙普利价值观，我们分析了与COVID-19在美国传播的因果关系的社会经济差异。我们研究了该疾病的几个阶段，以显示因果关系如何随着时间而变化。我们使用随机效应模型进行因果分析，并讨论两种方法之间的对应关系以验证我们的结果。我们显示了执行多变量分析时非线性机器学习模型的不同优势，而不是线性模型，尤其是因为机器学习模型可以映射数据中的非线性相关性。此外，因果沙普利值允许在计算机器学习模型计算的可变重要性中包括因果结构。
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.
