UC Berkeley

The recent increase in data and computing resource availability has made the use of data analytics more practical in practice than ever before. In particular, the ubiquity of technologies such as smartphones, wearable devices, and smart sensors has allowed for the collection of a large amount of individual level data. In contrast to traditional data analytics, which relies on wide sampling from a population to draw conclusions (i.e. cross-sectional sampling), this so called deep sampled data can be used through precision analytics to create customized experiences that better serve individuals and organizations. In this thesis, we explore this precision analytics framework that builds upon the fields of reinforcement learning and data driven decision making by extending their results to applications with individual level deep sampled data. One of the main applications of this framework is to systems where a decision maker is interested in applying an intervention (or policy) to affect the behavior of an individual agent or group of agents. Two key challenges that arise when analyzing such systems are that the decision maker may have scarce resources or high risk decisions that constrain how they apply their intervention, and that the decision maker may only have partial knowledge of how an agent will react to the intervention. The main focus of this thesis is to begin to analyze these challenges by providing predictive models that can accurately capture individual agent behavior, new estimation and machine learning techniques to efficiently estimate model parameters, and effective online and batch optimization methods to calculate these interventions. We will also discuss how these approaches can be implemented in practice, particularly in the precision healthcare setting.