Open utterances-bot opened 3 years ago
Really interesting post, thanks for sharing and for the resources!
Very informative, well organized, and particularly well written. Thanks.
Lovely post ! ...so well written and insightful. I would love to use some of your Mnemonic illustrations for my own teaching material (M. Sc course Data science in Bioinformatics
and B. Sc course An introduction to data science in R
, both at AArhus University, Denmark). Thanks Thomas.
Thanks for letting me know, @tbata ! Please help yourself! If you're interested, this deck version has a few more illustrations that didn't make the cut for the original blog. Specifically, slides 13-17 illustrate the mathematics behind deriving propensity score weights, and slide 26 briefly scratches the surface on some of the modern diff-in-diff alternatives.
Thanks for your quick answer and for sharing your slides These are neat illustrations !
By the way I just came across your talk at the u of Toronto reproducibility on line workshop.. also v interesting.
All the best Thomas
On 28 Mar 2021, at 13.22, Emily Riederer @.***> wrote:
Thanks for letting me know, @tbatahttps://github.com/tbata ! Please help yourself! If you're interested, this deck versionhttps://docs.google.com/presentation/d/1_gItoNO4lfrgrPfRCSlq833hIrw1iNZ2RcltCCkPspc/edit?usp=sharing has a few more illustrations that didn't make the cut for the original blog. Specifically, slides 13-17 illustrate the mathematics behind deriving propensity score weights, and slide 26 briefly scratches the surface on some of the modern diff-in-diff alternatives.
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Causal design patterns for data analysts | Emily Riederer
An informal primer to causal analysis designs and data structures
https://emilyriederer.netlify.app/post/causal-design-patterns/