A Data Science Central Community
A fundamental question faced by business analytics professionals and data scientists is whether they have a working correlative and causal explanatory model related to the phenomenon they are observing, be it related to reducing manufacturing error rates, determining the cause of customer abandonment, reducing fraud, targeting marketing, realizing logistics efficiencies, etc. This is known as an experimental model in science or a conceptual model in broader research venues (i.e. social…Continue
Added by Scott Mongeau on August 19, 2013 at 12:55am — No Comments
Improperly ‘conflating causation with correlation’ is a central but often overlooked danger in business analysis and data science initiatives. Especially with ‘big data’ sets, analysis will often reveal patterns that suggest a causal element which is only co-occurring phenomenon, or worse, ‘phantom phenomenon’ (i.e. coincidence or a happenstance of a limited dataset).
Some practical examples concerning mistaking correlation for causation: a recent letter to the editor in the…Continue
Added by Scott Mongeau on August 19, 2013 at 12:53am — No Comments