Drawing causal inferences from nonexperimental data is difficult due to the presence of confounders, variables that affect both the selection into treatment groups and the outcome. Matching methods can be used to subset the data to groups which are comparable with respect to important variables, but matching often fails to create sufficient balance between groups. Model-based matching is a nonparametric method for matching which groups observations that would be alike if none had received the treatment. We use model-based matching to conduct stratified permutation tests of association between the treatment and outcome, controlling for other variables. Under standard assumptions from the causal inference literature, model-based matching can be used to estimate average treatment effects.
The Berkeley Statistics Annual Research Symposium (BSTARS) surveys the latest research developments in the department, with an emphasis on possible applications to statistical problems encountered in industry. The conference consists of keynote lectures given by faculty members, talks by PhD students about their thesis work, and presentations of industrial research by alliance members. The day-long symposium gives graduate students, faculty, and industry partners an opportunity to connect, discuss research, and review the newest development happening on-campus and in the field of statistics.
Kellie Ottoboni is a former BIDS Data Science Fellow and a graduate of UC Berkeley's Department of Statistics. Her research at BIDS focused on using robust nonparametric statistics and machine learning to make causal inferences from data in the health and social sciences. The goal was to make reliable inferences while making minimal assumptions about the models generating the data. In addition to developing new statistical methods and studying their theoretical properties, Kellie wrote open source software implementing nonparametric methods in R and Python.