This post walks through my replication of the fuzzy regression discontinuity portion of Coppock and Green’s 2016 paper Is Voting Habit Forming? New Evidence from Experiments and Regression Discontinuities, and details some additional robustness checks I conducted. While I was able to reproduce all of their estimates fairly easily due to great replication materials, my additional robustness checks suggest that their results are more sensitive to bandwith choices than their testing suggests. Additionally, Coppock and Green argue the effects they find are likely due to habit alone, whereas I’m unconvinced that’s the sole mechanism involved. This is my work from Jennifer Hill and Joe Robinson-Cimpian’s Causal Inference class at NYU, and I’m grateful for both their feedback on the project.

This is the first of a few posts about some of the substantive and modeling findings from my master’s thesis, where I use Bayesian Additive Regression Trees (BART) and Poststratification to model support for a border wall from 2015-2019. In this post I explore some of the properties of using BART with varying intercepts (BART-vi) within the MRP framework.

Here, I use a bayesian hierarchical multinomial model to predict the first ballot results at the 2018 DFL (Democratic) State Convention, with data aggregated to the Party Unit level (ex: State Senate district) to guarantee anonymity. While using aggregated data obviously isn’t ideal, this sort of strategy shows a lot of promise, especially if individual level predictors could be harnessed as another level of the hierarchical model. As it stands, this is mostly a proof of concept for bayesian hierarchical models in this context. To use something like this in practice, one could use prior predictive simulation to game out the convention under various assumptions, or condition on the first ballot data and use it to analyze trends in support and predict subsequent ballots as your floor team collects further data.

This is a online version of the vignette for my r package retrodesign. It can be installed with:

When I was first studying probability theory as an undergrad, I had a bit of a conceptual hang-up with the Central Limit Theorem. Simulating it in R gave a nice visual of how each additional random variable smoothed out some of the original distribution’s individuality, and asymptotically we were left with a more generic shape. The proofs were relatively straightforward. One part, however, didn’t really make sense to me. My problem was this: Of all the many possible distributions, why is the normal distribution in particular that our i.i.d random variables converge to in distribution?