Research Projects

Estimation and inference of heterogeneous treatment effects (HTEs) have become a developing central interest in causal inference. The need arises from fields such as medicine, online marketing, policy studies, and government and welfare programs where the effect of a treatment is no longer viewed to be uniform across participants. In our work, we try to quantify and conduct statistical inferences of the HTEs mathematically. Specifically, we aim to perform valid inferences, where `validity’ stems from the fact that the covariate of interest may be chosen AFTER viewing the data. In that spirit, we aim to use sample splitting efficiently. Our methods will apply to observational data – finding a far-wide usage in fields such as epidemiology and precision medicine. Keywords: Causal Inference, Epidemiology, Precision Medicine, Heterogeneous Treatment Effect, Sample Splitting, Valid Statistical Inference