CausalKG: Causal Knowledge Graph Explainability Using Interventional and Counterfactual Reasoning
computer science, causality
Humans use causality and hypothetical retrospection in their daily decision-making, planning, and understanding of life events.1 The human mind, while retrospecting a given situation, think about questions such as “What was the cause of the given situation?,” “What would be the effect of my action?,” “What would have happened if I had taken another action instead?,” or “Which action led to this effect?” The human mind has an innate understanding of causality. It develops a causal model of the world, which learns with fewer data points, makes inferences, and contemplates counterfactual scenarios.8 The unseen and unknown scenarios are called “counterfactuals.”
Digital Object Identifier (DOI)
Published in IEEE Internet Computing, Volume 26, Issue 1, 2022.
© 2022, IEEE
Jaimini, U., & Sheth, A. (2022). CausalKG: Causal Knowledge Graph Explainability Using Interventional and Counterfactual Reasoning. IEEE Internet Computing, 26(1), 43–50. https://doi.org/10.1109/MIC.2021.3133551