Causal inference with the GLMM, Part 1| A. Solomon Kurz
I'm finally dipping my does into causal inference for quasi-experiments, and my first use case has missing data. In this post we practice propensity score matching with multiply-imputed data sets, and how to compute the average treatment effect for the treated (ATT) with g-computation.| A. Solomon Kurz
I've been thinking a lot about how to analyze pre/post control group designs, lately. Happily, others have thought a lot about this topic, too. The goal of this post is to introduce the change-score and ANCOVA models, introduce their multilevel-model counterparts, and compare their behavior in a couple quick simulation studies. Spoiler alert: The multilevel variant of the ANCOVA model is the winner.| A. Solomon Kurz
This post discusses briefly, the nix-shell environment for reproducible programming. In particular, there is an emphasis on extensions for installing and working with packages not in CRAN, i.e. packages off Github which are normally installed with devtools. Background The entire nix ecosystem is fantastic, and is the main packaging system used by d-SEAMS as well. Recently I began working through the excellent second edition of “Statistical Rethinking” by Richard McElreath1.| rgoswami.me
