Title: Causal Inference for Complex Observational Data
Date/Time: August 11, Thursday, 11:00 AM – 12:30 PM EST.
Abstract:
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.
Bio of the speaker:
Chuck Huber is the Director of Statistical Outreach at StataCorp and an 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 NetCourses, and gives talks about Stata at conferences and universities. Most of his current work is focused on statistical methods used by psychologists and other behavioral scientists. He has published in the areas of neurology, human and animal genetics, alcohol and drug abuse prevention, nutrition, and birth defects.
This webinar will be offered online via Zoom. Please register to receive the Zoom link prior to the webinar.
Registration link: https://libcal.dartmouth.edu/calendar/itc/2022DSAIW6. Click or tap if you trust this link." data-linkindex="7">https://libcal.dartmouth.edu/calendar/itc/2022DSAIW7. Click or tap if you trust this link." data-linkindex="8">libcal.dartmouth.edu/calendar/itc/2022DSAIW7
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Jianjun Hua
Statistical Consultant
Dartmouth College
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