We consider the use of interventions for resolving a problem of unidentified statistical models. The leading examples are from latent variable modelling, an influential statistical tool in the social sciences. We first explain the problem of statistical identifiability and contrast it with the identifiability of causal models. We then draw a parallel between the latent variable models and Bayesian networks with hidden nodes. This allows us to clarify the use of interventions for dealing with unidentified statistical models. We end by discussing the philosophical and methodological import of our result.

中文翻译：

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潜变量建模中的干预和可识别性

我们考虑使用干预措施来解决未知统计模型的问题。主要示例来自潜在变量建模，这是社会科学中一种有影响力的统计工具。我们首先解释统计可识别性的问题，并将其与因果模型的可识别性进行对比。然后我们在隐变量模型和带有隐藏节点的贝叶斯网络之间绘制一个平行线。这使我们能够澄清使用干预措施来处理未识别的统计模型。我们最后讨论了我们结果的哲学和方法论意义。