In the first two posts of this series, we relied on ordinary least squares (OLS). In the third post, we expanded to maximum likelihood for a couple logistic regression models. In all cases, we approached inference from a frequentist perspective. In this fourth post, we’re finally ready to make causal inferences as Bayesians. We’ll do so by refitting the Gaussian and binomial models from the previous posts with the Bayesian brms package ( Bürkner, 2017, 2018, 2022), and show how to comput...
