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Answering Causal Questions in AI
Introduction to some of the most common techniques which can be used in order to query information from data for interpretable inference.
Introduction
Two of the main techniques used in order to try to discover causal relationships are Graphical Methods (such as Knowledge Graphs and Bayesian Belief Networks) and Explainable AI. These two methods form in fact the basis of the Association level in the Causality Hierarchy (Figure 1), enabling us to answer questions such as: What different properties compose an entity and how are the different components related each other?
In case you are interested in finding out more about how Causality is used in Machine Learning, more information is available in my previous article: Causal Reasoning in Machine Learning.
Knowledge Graphs
Knowledge Graphs are a type of Graphical Technique commonly used in order to concisely store and retrieve related information from a large amount of data. Knowledge Graphs are currently widely used in applications such as querying information…