Retail Math: Ladder of Causation

The Wall Street Journal: “Suppose, for example, that a drugstore decides to entrust its pricing to a machine learning program that we’ll call Charlie. The program reviews the store’s records and sees that past variations of the price of toothpaste haven’t correlated with changes in sales volume. So Charlie recommends raising the price to generate more revenue. A month later, the sales of toothpaste have dropped—along with dental floss, cookies and other items. Where did Charlie go wrong?”

“Charlie didn’t understand that the previous (human) manager varied prices only when the competition did. When Charlie unilaterally raised the price, dentally price-conscious customers took their business elsewhere. The example shows that historical data alone tells us nothing about causes—and that the direction of causation is crucial.”

“Machine-learning systems have made astounding progress at analyzing data patterns, but that is the low-hanging fruit of artificial intelligence. To reach the higher fruit, AI needs a ladder, which we call the Ladder of Causation … To reach the higher rungs, in place of ever-more data, machines need a model of the underlying causal factors—essentially, a mathematics of cause and effect.”


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