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Webinar: Causal inference for complex observational data


Duration: 1 hour
Where: Join us from anywhere!
Cost: Free—but registrations are limited


Observational data often come with challenges that the data analyst needs to address. Treatment status or the exposure of interest may not be assigned randomly. Data are sometimes missing not at random (MNAR), which can lead to sample-selection bias. And statistical models for these data often need to account for unobserved confounding.

We show you how you can use standard maximum-likelihood estimation to fit extended regression models (ERMs) that deal with all of these common issues. We will work examples that demonstrate how to account for these observational data problems when they arise individually and when they occur simultaneously.

How to join

The webinar is free, but you must register to attend. Registrations are limited so register soon.

We will send you an email prior to the start with instructions on how to access the webinar.

Presenter: Chuck Huber

Chuck Huber portrait

Chuck Huber is Director of Statistical Outreach at StataCorp and Adjunct Associate Professor of Biostatistics at the Texas A&M School of Public Health and at the New York University School of Global 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 behavioral 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 survey sampling at NYU and introductory biostatistics at Texas A&M, where he previously taught categorical data analysis, survey data analysis, and statistical genetics.


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