Causal Effect Identifiability under Partial-Observability
Causal Effect Identifiability under Partial-Observability
复制标题
部分可观察性下的因果效应可识别性
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
E. Bareinboim
中科院分区：
文献类型：
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作者：
Sanghack Lee;E. Bareinboim
因果效应可识别性涉及在因果假设下，从观察或干预分布中确定干预一组变量对另一组变量的影响，这些假设通常以因果图的形式编码。这篇文献的大多数结果都隐含地假设图中建模的每个变量都是在可用的分布中测量的。然而，在实践中，所考虑的不同研究的数据收集并不一致地测量相同的变量。本文研究了当可用分布包含不同的变量集时因果效应的可辨识性问题，我们称之为部分可观测下的辨识问题。我们研究了在不同抽象水平下构成因果效应的因素的许多属性，然后根据它们相对于确定有针对性的干预措施的状态来描述它们之间的关系。我们建立了一个充分的图形标准，以确定是否可以从部分观测分布识别的影响。最后，在这些图形属性的基础上，我们开发了一个算法，该算法根据可用分布返回因果效应的公式。
Causal effect identifiability is concerned with establishing the effect of intervening on a set of variables on another set of variables from observational or interventional distributions under causal assumptions that are usually encoded in the form of a causal graph. Most of the results of this literature implicitly assume that every variable modeled in the graph is measured in the available distributions. In practice, however, the data collections of the different studies considered do not measure the same variables, consistently. In this paper, we study the causal effect identifiability problem when the available distributions encompass different sets of variables, which we refer to as identification under partial-observability. We study a number of properties of the factors that comprise a causal effect under various levels of abstraction, and then characterize the relationship between them with respect to their status relative to the identification of a targeted intervention. We establish a sufficient graphical criterion for determining whether the effects are identifiable from partially-observed distributions. Finally, building on these graphical properties, we develop an algorithm that returns a formula for a causal effect in terms of the available distributions.