posit::conf(2023) Workshop: Causal Inference with R
Register now: http://pos.it/conf Instructors: Malcolm Barrett and Travis Gerke 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 • are interested in understanding the fundamentals behind how to move from estimating correlations to causal relationships 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
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Transcript#
This transcript was generated automatically and may contain errors.
Hi, my name is Travis Gerke, and I'm excited to tell you about the top three reasons that you should take the causal inference with R workshop at PositConf 2023.
The first reason is that in this workshop, you will learn modern and elegant ways to perform causal inference using tools like tidymodels and the tidyverse. This contrasts with some more traditional ways of teaching causal inference that are very heavily theoretical or use more legacy programming approaches.
The second reason builds on the first reason. We have some fun coding elements in this workshop, such as performing causal inference with groupby and summarize. I bet you didn't think that was possible, but in this workshop, you'll find out how.
The third reason, of course, is the people. Malcolm Barrett has years of experience running this workshop successfully, and I'm sure you'll enjoy it. As for myself, it's my first year with the workshop, but I'm excited to come share my experience as a student of causal inference at Harvard School of Public Health, later a faculty member in academia studying causal inference and its applied methods, and now as a practitioner in the clinical trial space.
And of course, there's you. Together, we'll form a small community over two days of causal inference enthusiasts using R to implement it, and I'm really excited to see you there.
Together, we'll form a small community over two days of causal inference enthusiasts using R to implement it, and I'm really excited to see you there.
