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Understanding V-Structures and the Critical Role They Play in Causal Validation and Causal Inference
How to Detect and Correct the Direction of Causal Links in a Directed Acyclic Graph that are Incorrect When Compared to the Underlying Data
Introduction
Causal inference is an emerging field within machine learning that can move beyond predicting what could happen to explaining why it will happen and it doing so offers the promise of permanently resolving the underlying problem rather than dealing with the potential fallout.
Solving causal inference problems requires a visualisation of the cause-and-effect factors in a “Directed Acyclic Graph” or DAG which is typically developed by domain experts who have built up an informed view of the causality in a system or process.
A challenge with this approach is that the views of the domain experts can be flawed or biased and without an accurate DAG the results and outputs of causal models will be inaccurate and hence ineffective and the process of ensuring the DAG accurately represents the causality is called causal validation.
One specific problem within causal validation is detecting the direction of causality between two…
