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.

翻译：我们展示了Hahn等人提出的贝叶斯因果森林模型（BCF）如何用于估计2022年美国因果推断会议数据挑战中纵向数据集的条件平均处理效应。遗憾的是，现有BCF实现无法扩展到挑战数据的规模。为此，我们开发了flexBCF——一种更具可扩展性和灵活性的BCF实现——并将其用于我们的挑战提交。我们研究了结果对倾向得分估计方法选择及稀疏诱导回归树先验的敏感性。尽管我们发现整体点预测对这些建模选择并不特别敏感，但确实观察到使用灵活估计的倾向得分运行BCF通常能产生校准更优的不确定性区间。