logistic regression

Causal inference with Bayesian models

In this fourth post, we refit the models from the previous posts with Bayesian software, and show how to compute our primary estimates when working with posterior draws. The content will be very light on theory, and heavy on methods. So if you don’t love that Bayes, you can feel free to skip this one.

Causal inference with logistic regression

In this third post of the causal inference series, we switch to a binary outcome variable. As we will see, some of the nice qualities from the OLS paradigm fall apart when we want to make causal inferences with binomial models.

Sexy up your logistic regression model with logit dotplots

The major shortcoming in typical logistic regression line plots is they usually don’t show the data due to overplottong across the y-axis. Happily, new developments with Matthew Kay’s ggdist package make it easy to show your data when you plot your logistic regression curves. In this post I’ll show you how.