Causal Inference and Reasoning in Causally Insu-cient Systems
Causal Inference and Reasoning in Causally Insu-cient Systems
复制标题
因果不充分系统中的因果推断和推理
DOI：
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发表时间：
2006
期刊：
影响因子：
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通讯作者：
Jiji Zhang
中科院分区：
文献类型：
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作者：
Jiji Zhang
激发这篇论文的一个大问题是：在什么条件下，在多大程度上，被动观察可以告诉我们一组变量之间因果关系的结构，以及对一些变量的积极干预的潜在结果？这里特别关注的是一种常见的情况，在这种情况下，感兴趣的变量虽然本身是可测量的，但可能由于未观察到的共同原因而受到混淆。依靠称为最大祖先图的因果不足系统的图形表示，以及在文献中广泛讨论的两个众所周知的原理，因果马尔可夫条件和忠实条件，我们证明了FCI算法，一个在文献中用于从概率独立性和依赖性的事实推断未知因果结构特征的良好推理过程，在一些额外的良好推理规则下，是：在某种意义上说，任何未被推理过程决定的因果结构的特征，实际上是由概率独立性和依赖性的事实所不充分决定的。此外，我们考虑了定量推理的问题，局部干预的影响与fci -可学习的特征的未知的因果结构。我们改进和概括了文献中关于识别干预效果的两项重要工作。我们还对因果信度条件的可测试性作了一些初步的研究。
The big question that motivates this dissertation is the following: under what conditions and to what extent can passive observations inform us of the structure of causal connections among a set of variables and of the potential outcome of an active intervention on some of the variables? The particular concern here revolves around the common kind of situations where the variables of interest, though measurable themselves, may suffer from confounding due to unobserved common causes. Relying on a graphical representation of causally insufficient systems called maximal ancestral graphs, and two well-known principles widely discussed in the literature, the causal Markov and Faithfulness conditions, we show that the FCI algorithm, a sound inference procedure in the literature for inferring features of the unknown causal structure from facts of probabilistic independence and dependence, is, with some extra sound inference rules, also complete in the sense that any feature of the causal structure left undecided by the inference procedure is indeed underdetermined by facts of probabilistic independence and dependence. In addition, we consider the issue of quantitative reasoning about effects of local interventions with the FCI-learnable features of the unknown causal structure. We improve and generalize two important pieces of work in the literature about identifying intervention effects. We also provide some preliminary study of the testability of the Causal Faithfulness Condition.