Identification of Latent Variables From Graphical Model Residuals

arXiv:2101.02332 [cs, q-bio, stat]

This is a work in progress where we are developing an algorithm to recover the causal structure among observed variables in the presence of a latent confounder (an unobserved variable that drives multiple observed variables) that drives a large number of observed variables. An example of the latent confounders that we are interested in recovering are so-called master regulators which drives many genes on a gene network.

Fred Gruber
Fred Gruber
Senior Principal Scientist

My research interests include causal inference, Bayesian networks, causal discovery, machine learning.