Abstract

From medicine to marketing to social sciences, the promise of tailoring interventions to individual characteristics is undeniable. However, personalization often comes with costs— from logistical challenges to lack of shared context to concerns about fairness. In addition, personalized decision policies can be more fragile, because they typically require more data to learn accurately compared to identifying a single best intervention for all. In this talk I’ll introduce a new statistical estimator that quantifies, given historical data, if there is evidence that a personalized intervention policy provides significantly superior expected outcomes compared to deploying the best single overall intervention. We present results across four diverse datasets to highlight the wide range of settings where quantifying the impact of personalization can be helpful, and the strength of our proposed estimator over prior related approaches. Joint work with Zhaoqi Li.