ABSTRACT
In the field of explainable artificial intelligence (XAI), causal models and argumentation frameworks constitute two formal approaches that provide definitions of the notion of explanation. These symbolic approaches rely on logical formalisms to reason by abduction or to search for causalities, from the formal modeling of a problem or a situation. They are designed to satisfy properties that have been established as necessary based on the study of human-human explanations. As a consequence they appear to be particularly interesting for human-machine interactions as well. In this paper, we show the equivalence between a particular type of causal models, that we call argumentative causal graphs (ACG), and argumentation frameworks. We also propose a transformation between these two systems and look at how one definition of an explanation in the argumentation theory is transposed when moving to ACG. To illustrate our proposition, we use a very simplified version of a screening agent for COVID-19. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)
ABSTRACT
Dynamic Bayesian Network (DBN) is an useful tool to learn the causal inference and social network of random variables. In this article, we analyze the correlations between the spread of coronavirus (COVID-19) and certain self-reported COVID-19 indicators in the United States, and then adopt DBN model with search and score-based approach to analyze and interpret the causal relationships and social network between these variables by learning the structure of the Directed Acyclic Graph from the model. We explore the change of causality among fifty states during the pandemic of COVID-19 in the year of 2020 and interpret the root cause for changes and trends. We concentrate on five worst states with COVID-19 and then extended our studies to all states by comparing the causal relationships and analyzing the patterns of DAG. © 2021 IEEE.