论文标题
贝叶斯因果关系的计数潜在结果
Bayesian causal inference for count potential outcomes
论文作者
论文摘要
计数建模的文献提供了有用的工具来进行因果推断，当结果采用非阴性整数值时。应用于潜在结果框架，我们将贝叶斯因果推理文献与计数数据的统计模型联系起来。我们讨论了构建缺失潜在结果的预测后部的一般体系结构注意事项。讨论了估计平均治疗效果的特殊注意事项，有些将某些关系推广，而在因果推理文献中尚未遇到。
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.
