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Understanding Junctions (Chains, Forks, and Colliders) and the Role they Play in Causal Inference
Explaining junctions using correlation, independence and regression to understand their critical importance in causal inference
Introduction
Causal inference is the application of probability, visualisation, and machine learning in understanding the answer to the question “why?”
It is a relatively new field of data science and offers the potential to extend the benefits of predictive algorithms which address the symptoms of an underlying business problem to permanently curing the business problem by establishing cause and effect.
Typically causal inference will start with a dataset (like any other branch of data science) and then augment the data with a visual representation of the causes and effects enshrined in the relationships between the data items. A common form of this visualisation is the Directed Acyclic Graph or DAG.
The Problem
DAGs look deceptively simple but they hide a lot of complexity which must be fully understood to maximise the application of causal inference techniques.
