  The literature for count modeling provides useful tools to conduct causal
inference when outcomes take non-negative integer values. Applied to the
potential outcomes framework, we link the Bayesian causal inference literature
to statistical models for count data. We discuss the general architectural
considerations for constructing the predictive posterior of the missing
potential outcomes. Special considerations for estimating average treatment
effects are discussed, some generalizing certain relationships and some not yet
encountered in the causal inference literature.
