,
12 tweets,
4 min read
Read on Twitter
Award-winning, free material on #causalinference – here’s an overview on the 2013-2018 winners of the „Causality in Statistics Education Award“, awarded by .@AmstatNews, and initiated by .@yudapearl
In 2018, the award went to Jonas Peters, Dominik Janzing, and @bschoelkopf for their open-access textbook “Elements of Causal Inference”. This is the best textbook on the connection between machine learning and causal inference. It’s almost self-contained.
I think it is very useful for students in ML, stats, and physics, and is certainly very interesting for social scientists, because it views familiar topics from a different angle. mitpress.mit.edu/books/elements…
In 2017, the award went to Ilya Shpitser, for his course on Causal Inference. It has great discussions of the connection between CI and various statistical methods (g-formula, regularization) and is one of the few places where you will find worked examples on the ID algorithm and
other advanced stuff. amstat.org/asa/files/zipf…
In 2016, the award went to @oacarah and Arvid Sjölander for their respective CI courses. Both have great slides. Arah’s course has a lot on measurement error. Sjölander has interesting problem sets.
Here is the link to Arah’s course material: dropbox.com/sh/mzuy3bewepw…
And here is the link to Sjölander’s course material: amstat.org/misc/causaliit…
In 2015, the award went to Tyler VanderWeele, for his (non-open-access) textbook “Explanation in Causal Inference: Methods for Mediation and Interaction”. Also has material on spillover effects, generally quite thorough on both the math and implementation in, err, SAS.
In 2014, the award went to Maya Petersen and @LauraBBalzer for their CI course. It has all the basics & quite a lot on targeted maximum likelihood estimation. ucbbiostat.com
In 2013, the award went to Felix Elwert, who has various slides on causal graphs, including many example on endogenous selection ssc.wisc.edu/~felwert/causa….
The 2019 winners are Peter Steiner and myself.
