In this tutorial, I’ll present an overview of our book, Causal Inference in R, freely available at r-causal.org. We’ll discuss the whole game, so to speak, of causal inference, following a few key steps: 1. Specify a causal question 2. Draw our assumptions using a causal diagram 3. Model our assumptions 4. Diagnose our models 5. Estimate the causal effect, and 6. Conduct sensitivity analysis on the effect estimate. We’ll discuss some new tools in the causal inference ecosystem, such as tipr, ggdag, propensity, halfmoon, and more, each making the act of causal inference easier and more principled.
Malcolm Barrett, Stanford University
malco.io/
Malcolm Barrett is an epidemiologist and research software engineer at Stanford University. After receiving his Ph.D. in epidemiology from the University of Southern California, he worked as a data scientist at Apple and Posit. His work has focused on causal inference methodology and software development, including many R packages for causal inference. Collectively, open-source tools he has authored have millions of downloads.
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