In both data science and academic research, prediction modeling is often not enough; to answer many questions, we need to approach them causally. In this workshop, we’ll teach the essential elements of answering causal questions in R through causal diagrams, and causal modeling techniques such as propensity scores and inverse probability weighting. We’ll also show that by distinguishing predictive models from causal models, we can better take advantage of both tools. You’ll be able to use the tools you already know--the tidyverse, regression models, and more--to answer the questions that are important to your work.
This course will be appropriate for you if you answer yes to these questions:
9:00 A.M. – 5:00 P.M.Register
Lucy D’Agostino McGowan is an assistant professor in the Mathematics and Statistics Department at Wake Forest University. She received her PhD in Biostatistics from Vanderbilt University and completed her postdoctoral training at Johns Hopkins University Bloomberg School of Public Health. Her research focuses on statistical communication, causal inference, data science pedagogy, and human-data interaction. Dr. D’Agostino McGowan is the past chair of the American Statistical Association’s Committee on Women in Statistics, chair elect for the Section on Statistical Graphics, and can be found blogging at livefreeordichotomize.com, on Twitter @LucyStats, and podcasting on the American Journal of Epidemiology partner podcast, Casual Inference.
Malcolm Barrett is a data scientist and an epidemiologist. During his Ph.D., he studied vision loss, focusing on epidemiologic methods. He's since worked in the private sector, including Teladoc Health and Apple. Malcolm is also the author of several causal inference-focused R packages, such as ggdag and tidysmd. He regularly contributes to other open source software, including favorite community projects like usethis, ggplot2, R Markdown.