Chuck Huber: Causal Inference for Complez Observational Data

Date: 
July 30, 2019
Time: 
1:00 - 2:00 PM
Place: 
Mission Hall 1401

Observational data often have issues which present challenges for the data analyst.  The treatment status or exposure of interest is often not assigned randomly.  Data are sometimes missing not at random (MNAR) which can lead to sample selection bias.  And many statistical models for these data must account for unobserved confounding.  This talk will demonstrate how to use standard maximum likelihood estimation to fit extended regression models (ERMs) that deal with all of these common issues alone or simultaneously. 

Event Type: 
PhD Program Sponsored Event