The Economic Engineering of Personalized Experiences ABSTRACT Consumer applications employ algorithms to deliver personalized experiences to users, among others, in search, e-commerce, online streaming, and social media, impacting how users spend their time and money. The dissertation studies the design of such personalization algorithms and the social consequences of their deployment. The first chapter analyzes how preference measurement error differentially affects user groups in optimal personalization. Under such measurement error, welfare maximization is incompatible with equalizing the utility of (statistical)…

Causal Inference Under Privacy Constraints ABSTRACT Causal inference is an important tool for learning the effects of interventions in observational or experimental settings. It is widely used in many fields such as epidemiology, economics, and political science to find answers like the average treatment effect of a medical procedure or the individual treatment effect of a personalized ad campaign. In commercial applications, the era of big data allows companies to increase their experiment volume, incentivizing them, in turn, to collect…