Correlation or causality

 

In episode “Correlation or causality?” David Wells, VP and co-founder of Cigar Sense, explains the key principles that drive the accuracy of our cigar recommendations. He also talks about the differences between online recommendations based on data correlation, aka big data analytics,  as opposed to recommendations based on data causality, aka deep data analytics.

In statistics, “correlation does not prove causality” refers to the inability to legitimately deduce a cause-and-effect relationship between two variables solely on the basis of an observed association or correlation between them.

Very simply said, on one hand you can invest all you can on marketing, get millions of consumer data for and through correlation based recommendations, and hope you will make at least 50% of those millions happy with what they buy next. On the other hand, you can invest in a sensory panel, in samples, in training, in curing the data, in continuously monitoring the algorithm performance over thousands of consumers and make 90%+ of them happy. We believe that, belonging to the second example, we are ready to scale up.

On another note, correlation and causality are obviously also referred to when science attempts to demonstrate flavor precursors from different terroirs. Are they managing to find causation? More on this, and on a lot more, in our online tasting course. Stay tuned.

 

Listen to podcast episode 42, “Correlation or causality?”

here or on your favorite podcast player app

 

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This podcast is brought to you by Cigar Sense founders and paid members. With the free Cigar Sense membership, you can obtain personalized recommendations of cigars that best suit your unique individual tastes. In a few simple steps, you can see what your top cigar picks are from the thousands of cigars available on the market. You can also explore sensory and other cigar attributes as Cigar Sense helps you better understand what you like and dislike in a cigar.

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