Causal Inference for Complex Observational Data with Stata

When: November 02, 2020. 1:30 PM (EST)

To Register, please visit https://tinyurl.com/causal-inference-webinar.

Abstract:

Observational data often present unique challenges.  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 MNAR 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 the common issues either alone or simultaneously.  

Presenter: Chuck Huber, Ph.D. [StataCorp]

Chuck Huber is Director of Statistical Outreach at StataCorp and Adjunct Associate Professor of Biostatistics at the Texas A&M School of Public Health. In addition to working with Stata’s team of software developers, he produces instructional videos for the Stata YouTube channel, writes blog entries, develops online Net Courses and gives talks about Stata at conferences and universities. 

Most of his current work is focused on statistical methods used by behavioural and health scientists.  He has published in the areas of neurology, human and animal genetics, alcohol and drug abuse prevention, nutrition and birth defects.  Dr. Huber currently teaches introductory biostatistics at Texas A&M where he previously taught categorical data analysis, survey data analysis, and statistical genetics.