论文标题
贝叶斯因果森林和2022 ACIC数据挑战：可伸缩性和灵敏度
Bayesian Causal Forests & the 2022 ACIC Data Challenge: Scalability and Sensitivity
论文作者
论文摘要
我们演示了Hahn等人的贝叶斯因果森林模型（BCF）如何用于估计2022年2022年美国因果推理会议数据挑战中纵向数据集的有条件平均治疗效果。不幸的是，现有的BCF实现并不能扩展到挑战数据的规模。因此，我们开发了BCF的FlexBCF（更可扩展，更灵活的实现），并将其用于我们的挑战提交中。我们研究了结果对倾向得分估计方法选择的敏感性以及诱导稀疏回归树先验的使用。尽管我们发现我们的总体预测对这些建模选择并不特别敏感，但我们确实观察到，以灵活的估计倾向得分运行BCF通常会产生更好地校准的不确定性间隔。
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.
