存在不可测变量时的因果关系检测
Detecting Causal Relations in the Presence of Unmeasured Variables
摘要：
潜在变量的存在会使从统计数据中推断测量变量之间的因果关系变得非常复杂。在许多情况下，潜在变量的存在使得不可能确定两个测量变量A和B，是否A导致B，B导致A，或者有一些共同的原因。在本文中，我提出了几个定理，状态条件下，它是可能的可靠地推断两个测量变量之间的因果关系，无论是否潜变量的行为或不。
英文摘要：
The presence of latent variables can greatly complicate inferences about causal relations between measured variables from statistical data. In many cases, the presence of latent variables makes it impossible to determine for two measured variables A and B, whether A causes B, B causes A, or there is some common cause. In this paper I present several theorems that state conditions under which it is possible to reliably infer the causal relation between two measured variables, regardless of whether latent variables are acting or not.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
