Causal Inference in R

Photo by Nadir sYzYgY on Unsplash


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, inverse probability weighting, and g-computation. We’ll also show that by distinguishing predictive models from causal models, we can better take advantage of both tools; prediction modeling plays a role in establishing many causal models, such as propensity scores and g-computation. 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.

Jul 29, 2020 9:00 AM
useR! 2020
Malcolm Barrett
Data Scientist

I am a data scientist, an R developer, and an epidemiologist. My work in public health has spanned on-ground clinical education and research for clinical and cohort studies. Previously, I was an intern at RStudio, and I served two years in AmeriCorps at federally-qualified health centers in Michigan and New York City.
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