Machine Learning Times
Machine Learning Times
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7 months ago
Measuring Invisible Treatment Effects with Uplift Analysis

  Models make predictions by identifying consistent correlations in what has been observed, but we usually require more than predictions to know what action we should take. For example, knowing that older people are more likely to have heart disease is a good first step, but knowing behaviors or treatments that will reduce the risk of heart disease as we age is actionable. Knowing millennials are more likely to buy your product than gen Z is nice, but knowing which marketing approach will persuade gen Z to buy is valuable. In this election season, knowing who will vote

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