In the third post in this series, we extended out counterfactual causal-inference framework to binary outcome data. We saw how logistic regression complicated the approach, particularly when using baseline covariates. In this post, we’ll practice causal inference with unbounded count data, using the Poisson and negative-binomial likelihoods. We need data We’ll be working with a subset of the epilepsy data from the brms package. Based on the brms documentation (execute ?
