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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.

Pier Paolo Ippolito
Towards Data Science
7 min readAug 11, 2021

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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.

Figure 1: Causality Hierarchy (Image by Author).

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…

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Towards Data Science
Towards Data Science

Published in Towards Data Science

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Pier Paolo Ippolito
Pier Paolo Ippolito

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