Hello ASA community! This is a reminder of the ASA traveling course hosted by the Orange County/Long Beach chapter that will be held on Saturday, 9/14.
TO PURCHASE TICKETS AND FOR MORE INFORMATION PLEASE VISIT:
Link to tickets
Instructor:
Babette Brumback, Professor Emerita, University of Florida Department of Biostatistics
Course description:
One of the primary motivations for clinical trials and observational studies of humans is to infer cause and effect. Disentangling causation from confounding is of utmost importance. Fundamentals of Causal Inference with R explains and relates different methods of confounding adjustment in terms of potential outcomes and graphical models, including standardization, doubly robust estimation, difference-in-differences estimation, and instrumental variables estimation. Several real data examples, simulation studies, and analyses using R motivate the methods throughout. The course assumes familiarity with basic statistics and probability, regression, and R. The course will be taught with a blend of lecture and worked examples.
COURSE OUTLINE
Module 1: 8:00-9:30am
Introduction
a) Definitions and Datasets
b) Potential Outcomes Framework
c) Directed Acyclic Graphs
Break 15 mins
Module 2: 9:45-11:15am
Adjusting for Confounding Part 1
a) Standardization
b) Difference-in-Differences Estimation
Break 1 hour
Module 3: 12:15-1:45pm
Adjusting for Confounding Part 2
c) Front-Door Estimation
d) Instrumental Variables Estimation
Comparison of the Four Methods
Break 15 mins
Module 4: 2:00-3:30pm
More Advanced Topics
a) Mediation
b) Time-Dependent Confounding
About the Instructor:
Babette Brumback is known for her work on causal inference. She is the author of the recently published textbook, Fundamentals of Causal Inference: With R. Babette is Professor Emerita of Biostatistics at the University of Florida, and she is an elected member of Delta Omega and a Fellow of the American Statistical Association. Babette's statistical research has concentrated on methods for longitudinal data analysis, causal modeling, bias adjustment, and analysis of data from complex sampling designs. She has also collaborated extensively on public health and medical studies concerning a broad array of research areas.
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Lindsay Younis
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