Fundamentals of Causal Inference: With R
Instructor: Babette Brumback, Professor Emerita, University of Florida Department of Biostatistics
Full-day course
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:
Introduction
Potential Outcomes and Effect Measures
Causal Directed Acyclic Graphs
Standardization and Doubly Robust Estimation
Difference-in-Differences Estimation
Instrumental Variables Estimation
Additional Topics and Examples for the Full Day Course
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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