Unsupervised Learning

Modeling Latent Effect Heterogeneity

This project seeks to develop semi-parametric Bayesian regression models to estimate latent heterogeneity in the effect of treatment variables and/or observed covariates.

Unsupervised Learning Methods and FMM for Election Forensics (with Walter Mebane)

This is a NFS-funded project with Walter Mebane to develop positive models to detect fraud in elections. The project uses unsupervised learning methods to estimate probability of fraud using election data.