发表机构
Iowa State University（爱荷华州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对连续结果下部分识别问题，提出一种贝叶斯方法，为多变量或连续结果的一大类因果量推导概率界，反映不同置信水平。
AI 中文摘要
部分识别问题关注在给定观测分布和底层结构因果模型（SCM）的因果图时，对仍无法识别的因果量进行界定。虽然针对连续结果已开发出某些因果量的界，但这些界必然是单变量的，且无法反映不同的置信水平。在部分识别先前工作的基础上，我们提出了一种简单的贝叶斯方法，用于推导一大类可能具有多变量和/或连续结果变量的因果量的概率界。
英文摘要
The problem of partial identification concerns bounding causal quantities that remain unidentifiable given the observed distribution and the causal diagram of the underlying structural causal model (SCM). While bounds have been developed for certain causal quantities with continuous outcomes, they are necessarily univariate and are unable to reflect differing levels of confidence. Building on previous work in partial identification, we propose a simple Bayesian method for deriving probabilistic bounds on a large class of causal quantities with potentially multivariate and/or continuous outcome variables.
