论文标题
重新访问常规可识别性问题
Revisiting the General Identifiability Problem
论文作者
论文摘要
我们回顾了最初在[Lee等人，2019]中引入的一般可识别性问题，以进行因果推理，并注意，有必要将观察性分布的积极性假设添加到问题的原始定义中。我们表明，如果没有这样的假设，则do-calculus的规则，因此[Lee等人，2019年]中提出的算法并不声音。此外，添加假设将导致[Lee等，2019]中的完整性证明失败。在积极的假设下，我们提出了一种新的算法，既是声音又完整的。这种新算法的一个不错的属性是，它通过将一般可识别性问题分解为一系列经典可识别性子问题，从而通过Pearl [1995]建立了通用可识别性和经典可识别性之间的联系。
We revisit the problem of general identifiability originally introduced in [Lee et al., 2019] for causal inference and note that it is necessary to add positivity assumption of observational distribution to the original definition of the problem. We show that without such an assumption the rules of do-calculus and consequently the proposed algorithm in [Lee et al., 2019] are not sound. Moreover, adding the assumption will cause the completeness proof in [Lee et al., 2019] to fail. Under positivity assumption, we present a new algorithm that is provably both sound and complete. A nice property of this new algorithm is that it establishes a connection between general identifiability and classical identifiability by Pearl [1995] through decomposing the general identifiability problem into a series of classical identifiability sub-problems.
