Episode sponsored by Tidelift: tidelift.com
We already mentioned multilevel regression and post-stratification (MRP, or Mister P) on this podcast, but we didn’t dedicate a full episode to explaining how it works, why it’s useful to deal with non-representative data, and what its limits are. Well, let’s do that now, shall we?
To that end, I had the delight to talk with Lauren Kennedy! Lauren is a lecturer in Business Analytics at Monash University in Melbourne, Australia, where she develops new statistical methods to analyze social science data. Working mainly with R and Stan, Lauren studies non-representative data, multilevel modeling, post-stratification, causal inference, and, more generally, how to make inferences from the social sciences.
Needless to say that I asked her everything I could about MRP, including how to choose priors, why her recent paper about structured priors can improve MRP, and when MRP is not useful. We also talked about missing data imputation, and how all these methods relate to causal inference in the social sciences.
If you want a bit of background, Lauren did her Undergraduates in Psychological Sciences and Maths and Computer Sciences at Adelaide University, with Danielle Navarro and Andrew Perfors, and then did her PhD with the same advisors. She spent 3 years in NYC with Andrew Gelman’s Lab at Columbia University, and then moved back to Melbourne in 2020. Most importantly, Lauren is an adept of crochet — she’s already on her third blanket!
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !
Thank you to my Patrons for making this episode possible!
Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O’Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Vincent Arel-Bundock, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege and Rémi Louf.
Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag 😉
Links from the show:
- Lauren’s website: https://jazzystats.com/
- Lauren on Twitter: https://twitter.com/jazzystats
- Lauren on GitHub: https://github.com/lauken13
- Improving multilevel regression and poststratification with structured priors: https://arxiv.org/abs/1908.06716
- Using model-based regression and poststratification to generalize findings beyond the observed sample: https://arxiv.org/abs/1906.11323
- Lauren’s beginners Bayes workshop: https://github.com/lauken13/Beginners_Bayes_Workshop
- MRP in RStanarm: https://github.com/lauken13/rstanarm/blob/master/vignettes/mrp.Rmd
- Choosing your rstanarm prior with prior predictive checks: https://github.com/stan-dev/rstanarm/blob/vignette-prior-predictive/vignettes/prior-pred.Rmd
- Mister P — What’s its secret sauce?: https://statmodeling.stat.columbia.edu/2013/10/09/mister-p-whats-its-secret-sauce/
- Bayesian Multilevel Estimation with Poststratification — State-Level Estimates from National Polls: https://pdfs.semanticscholar.org/2008/bee9f8c2d7e41ac9c5c54489f41989a0d7ba.pdf
- MRPyMC3 – Multilevel Regression and Poststratification with PyMC3: https://austinrochford.com/posts/2017-07-09-mrpymc3.html
- Using Hierarchical Multinomial Regression to Predict Elections in Paris districts: https://www.youtube.com/watch?v=EYdIzSYwbSw
- Regression and Other Stories book: https://www.cambridge.org/fr/academic/subjects/statistics-probability/statistical-theory-and-methods/regression-and-other-stories?format=PB
- Bayesian Nonparametric Modeling for Causal Inference, by Jennifer Hill: https://www.tandfonline.com/doi/abs/10.1198/jcgs.2010.08162
- Lauren’s Data Ethics course: https://anastasiospanagiotelis.netlify.app/teaching/dataviza2019/lectures/04dataethics/ethicaldatascience#1