from social media trends to family dynamics, social interactions shape our daily lives. in this talk, i will present tools i have developed for statistical inference and decision-making in light of these social interactions.

(1) inference: i will talk about estimation of causal effects in the presence of interference. in causal inference, the term “interference” refers to a situation where, due to interactions between units, the treatment assigned to one unit affects the observed outcomes of others. i will discuss large-sample asymptotics for treatment effect estimation under network interference where the interference graph is a random draw from a graphon. when targeting the direct effect, we show that popular estimators in our setting are considerably more accurate than existing results suggest. meanwhile, when targeting the indirect effect, we propose a consistent estimator in a setting where no other consistent estimators are currently available.

(2) decision-making: turning to reinforcement learning amid social interactions, i will focus on a problem inspired by a specific class of mobile health trials involving both target individuals and their care partners. these trials feature two types of interventions: those targeting individuals directly and those aimed at improving the relationship between the individual and their care partner. i will present an online reinforcement learning algorithm designed to personalize the delivery of these interventions. the algorithm's effectiveness is demonstrated through simulation studies conducted on a realistic test bed, which was constructed using data from a prior mobile health study. the proposed algorithm will be implemented in the adapts hct clinical trial, which seeks to improve medication adherence among adolescents undergoing allogeneic hematopoietic stem cell transplantation.