Causal Inference in R

Abstract

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.