Learn Stan with brms, Part III | A. Solomon Kurz
y ~ 1 + x| A. Solomon Kurz
One of the nice things about the simple OLS models we fit in the last post is they’re easy to interpret. The various \(\beta\) parameters were valid estimates of the population effects for one treatment group relative to the wait-list control.1 However, this nice property won’t hold in many cases where the nature of our dependent variables and/or research design requires us to fit other kinds of models from the broader generalized linear mixed model (GLMM) framework.| A. Solomon Kurz
Welcome to the beginning This is the first post in a series on causal inference. Our ultimate goal is to learn how to analyze data from true experiments, such as RCT’s, with various likelihoods from the generalized linear model (GLM), and with techniques from the contemporary causal inference literature. We’ll do so both as frequentists and as Bayesians. I’m writing this series because even though I learned a lot about data analysis and research design during my PhD, I did not receive t...| A. Solomon Kurz
| xcelab.net
