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SERtalks - parametric and semi-parametric estimators for causal inference

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53-105 CHS

650 Charles E Young Dr South

Los Angeles, CA 90095

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Speakers:

Laura Balzer
Assistant Professor
University of Massachusetts-Amherst
School of Public Health and Health Sciences


Jennifer Ahern
Associate Dean for Research
Associate Professor
University of California, Berkeley
School of Public Health


Schedule Breakdown:

09:00 am - 10:30 am – lecture
10:30 am - 10:45 am – break
10:45 am - 12:15 pm – lecture
12:15 pm - 12:45 pm – light lunch break
12:45 pm - 01:45 pm – lecture

This workshop will introduce participants to a “causal roadmap” approach to epidemiologic questions: 1) clear statement of the scientific question, 2) definition of the causal model and parameter of interest, 3) assessment of identifiability – that is, linking the causal effect to a parameter estimable from the observed data distribution, 4) choice and implementation of estimators including parametric and semi-parametric, and 5) interpretation of findings. The focus will be on estimation with a simple substitution estimator (parametric g-computation), inverse probability of treatment weighting (IPTW), and targeted maximum likelihood estimation (TMLE) with Super Learner. Participants will work through the roadmap using an applied example and implement these estimators in R during the workshop session.

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53-105 CHS

650 Charles E Young Dr South

Los Angeles, CA 90095

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