Abstract : 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 two ad hoc modeling choices we made during our initial submission: (i) the choice of propensity score estimation method and (ii) 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.
A pre-print is available here.
Recommended citation: Kokandakar, A.H., Kang, H., and Deshpande, S.K. (2022+). "Sensitivity of Bayesian causal forests to modeling choices: A re-analysis of the 2022 ACIC Data Challenge." arXiv preprint arXiv:2210.02020