General Identifiability with Arbitrary Surrogate Experiments
General Identifiability with Arbitrary Surrogate Experiments
复制标题
任意替代实验的一般可识别性
DOI：
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发表时间：
2019
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;Juan David Correa;E. Bareinboim
我们研究的问题，从任意收集的观察和实验分布的因果关系识别，并根据调查，这通常是在一个因果图的形式的现象的实质性知识。我们称这个问题为g-可识别性，简称gID。gID设置包含因果推理中的两个众所周知的问题，即可识别性[Pearl，1995]和z-可识别性[Bareinboim和Pearl，2012] -前者假设观测分布必然可用，并且无法进行实验，这两个条件在gID设置中都是放松的;后者假设所有实验组合都可用，即，实验集Z的幂集，其中gID不需要先验。在本文中，我们介绍了一种通用的策略来证明非gID的基础上hedgelets和灌木丛，这导致了相应的决策问题的一个必要和充分的图形条件。我们进一步开发了一个系统地计算目标效果的过程，并证明了它是健全的和完整的gID实例。换句话说，算法返回表达式失败意味着目标效应无法从可用分布计算。最后，作为这些结果的推论，我们表明，做演算是完整的g-可识别性的任务。
We study the problem of causal identification from an arbitrary collection of observational and experimental distributions, and substantive knowledge about the phenomenon under in-vestigation, which usually comes in the form of a causal graph. We call this problem g-identifiability , or gID for short. The gID setting encompasses two well-known problems in causal inference, namely, identifiability [Pearl, 1995] and z-identifiability [Bareinboim and Pearl, 2012] — the former assumes that an observational distribution is necessarily available, and no experiments can be performed, conditions that are both relaxed in the gID setting; the latter assumes that all combinations of experiments are available, i.e., the power set of the experimental set Z , which gID does not require a priori. In this paper, we introduce a general strategy to prove non-gID based on hedgelets and thickets , which leads to a necessary and sufficient graphical condition for the corresponding decision problem. We further develop a procedure for systematically computing the target effect, and prove that it is sound and complete for gID instances. In other words, failure of the algorithm in returning an expression implies that the target effect is not computable from the available distributions. Finally, as a corollary of these results, we show that do-calculus is complete for the task of g-identifiability.