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Measuring Invisible Treatment Effects with Uplift Analysis: A Get-Out-The-Vote Example


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 is interesting, but identifying unlikely voters who can be persuaded to show up at the polls is everything to campaign managers. When data science goes further to estimate the impact of alternative actions we may perform to achieve a better outcome, we call it uplift modeling or, more technically, treatment effect modeling. For this instructional blog we will use a very limited example of how uplift modeling can apply to get-out-the-vote campaigns, without divulging which sample or geography was used.



This post first appeared on Elder Research Data Science & Machine Learning Blog, please read the originial post: here

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Measuring Invisible Treatment Effects with Uplift Analysis: A Get-Out-The-Vote Example

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