Introduction to Causality

Peer reviewed: 
No, item is not peer reviewed.
Scholarly level: 
Graduate student (PhD)
Date created: 
2020-12-18
Keywords: 
Causality
Deep learning
Machine learning
Paradox
Statistics
Do operator
Abstract: 

In scientific discovery, engineering, imaging, and machine learning, it is often critical to understand what causes an event or observation, rather than focusing on correlation/association alone. In order to make this complex topic more accessible I would like to share what I learned on how causality can be applied and what concepts are essential in doing so. I will introduce the graphical causal model (Bayesian network), and show how you can translate human intuition on causality into formal axioms that fuse the causal graph with the probability space from observed events. We will discuss counterfactual causality, and end with an overview of recommendations on how to use causality in practice as well as current open issues and relevant papers that tackle those questions.

Description: 

Presentation in research meeting, Dec 18 2020.

Language: 
English
Document type: 
Lecture / Talk
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