  We demonstrate how Hahn et al.'s Bayesian Causal Forests model (BCF) can be
used to estimate conditional average treatment effects for the longitudinal
dataset in the 2022 American Causal Inference Conference Data Challenge.
Unfortunately, existing implementations of BCF do not scale to the size of the
challenge data. Therefore, we developed flexBCF -- a more scalable and flexible
implementation of BCF -- and used it in our challenge submission. We
investigate the sensitivity of our results to the choice of propensity score
estimation method and the use of sparsity-inducing regression tree priors.
While we found that our overall point predictions were not especially sensitive
to these modeling choices, we did observe that running BCF with flexibly
estimated propensity scores often yielded better-calibrated uncertainty
intervals.
