接下来应该测量什么？通过细化机制寻找识别策略
What Should We Measure Next? Finding Identification Strategies by Refining Mechanisms
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中文总结 AI 辅助
本文研究部分设定因果模型中的迭代识别问题，给出观测拦截直接效应或混杂路径的变量能使不可识别查询可识别的充分必要条件图条件，并提出高效算法定位所有此类机会，帮助分析者导航模型空间。
中文摘要 AI 辅助
因果推断中的经典方法将模型设定视为固定的，假设研究者直接将所有相关的领域知识转化为因果模型，然后利用该模型推导其逻辑含义。然而，在实际应用中，模型设定往往是一个迭代过程，涉及探索与反思：在人们可以研究的众多现象方面中，哪些方面实际上对感兴趣因果效应的识别至关重要？本文研究了部分设定因果模型中的迭代识别问题。我们专注于确定在何种情况下，观测拦截两个变量之间直接效应或混杂路径的变量，能够在半马尔可夫模型中实现识别。我们给出了观测此类变量能使不可识别查询变得可识别的充分必要条件图条件，并提出了一个高效算法，用于在给定因果图中定位所有此类机会。我们的结果通过将注意力引向研究者实质性知识中可能促成成功识别策略的部分，有助于分析者更好地在模型空间中导航。
英文摘要
Canonical approaches in causal inference treat model specification as fixed, assuming that researchers directly translate all relevant domain knowledge into a causal model, which can then be used to deduce its logical implications. Yet, in practical applications, model specification is often an iterative process, and involves exploration and introspection: of the many aspects of the phenomenon one could investigate, which ones actually matter for the identification of the causal effect of interest? In this paper we study the problem of iterative identification in partially specified causal models. We focus on determining where observing variables that intercept a direct effect or a confounding path between two variables could enable identification in semi-Markovian models. We give necessary and sufficient graphical conditions for when observing such variables can render an unidentifiable query identifiable, together with an efficient algorithm for locating all such opportunities in a given causal diagram. Our results can help analysts better navigate the model space by drawing attention to the parts of their substantive knowledge that could result in a successful identification strategy.
