Causal Inference with R

2-day workshop
Instructor

Malcolm Barrett & Travis Gerke

Starts on

September 17, 2023

Description

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.

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.

Audience

This course is for you if you:

  • know how to fit a linear regression model in R,

  • have a basic understanding of data manipulation and visualization using tidyverse tools, and

  • are interested in understanding the fundamentals behind how to move from estimating correlations to causal relationships.

Instructors

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.
Travis Gerke is a post-academic leading data science teams for clinical trials. In his graduate training (AM Biostatistics and ScD Epidemiology, Harvard), Travis was fortunate to learn from key leaders in the causal inference domain, and he is thrilled to pass along relevant insights in this workshop. Like Malcolm, Travis enjoys developing R packages (shinyDAG, ggconsort, ggswimlane), and is an active member of the wonderful R community.