https://doi.org/10.1109/MIC.2021.3133551

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Title

CausalKG: Causal Knowledge Graph Explainability Using Interventional and Counterfactual Reasoning

ORCID iD

Jaimini: 0000-0002-1168-0684

Sheth: 0000-0002-0021-5293

Document Type

Article

Subject Area(s)

computer science, causality

Abstract

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)

https://doi.org/10.1109/MIC.2021.3133551

APA Citation

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

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