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I've been getting used to gganimate and thought it would be useful to put together some illustrations of what various causal inference methods *actually do to data* and how they work. Here, for example, is what it means to control for a (binary) variable
Here is how one version of matching works
Instrumental variables (the Wald estimator, to be specific)
Fixed effects (really just the animation for controlling again, but with more categories)
Difference-in-differences
And finally (a simplified version of) RDD
This is for a class I'm designing on programming and causal inference (h/t @causalinf) designed to go BEFORE the rest of the econometrics sequence. The idea is teaching concepts before methods. Notice that none of these graphs use regression! It's not necessary!
Here's a link to a page with these graphs alongside DAGs and more explanatory detail, if you want something to link your students to. Also if you have any ideas for other methods I should animate let me know. nickchk.com/causalgraphs.h…
Code now available in a repo: github.com/NickCH-K/causa…
Update for anyone still looking at this version of the thread. New version of the thread is up, with slower graphs:
