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Demystifying Dependence and Why it Is Important in Causal Inference and Causal Validation
A step-by-step guide in understanding the concept of dependence and how to apply it to validate directed acyclic graphs using Python
Introduction
Causal Inference is an emergent branch of data science concerned with determining the cause-and-effect relationship between events and outcomes and it has the potential to significantly add to the value that machine learning can generate for organisations.
For example, a traditional machine learning algorithm can predict which loan customers are likely to default thereby enabling proactive intervention with customers. However, although this algorithm will be useful to reduce loan defaults, it will have no concept of why they occurred and whilst pro-active intervention is useful knowing the reasons for defaults would enable the underlying cause to be addressed. In this world pro-active intervention may no longer be necessary because the factors that lead to defaulting have been permanently cured.
This is the promise of Causal Inference and why it has the potential to deliver significant impact and outcomes to those organisations that can harness that potential.
