利用冗余检验发现并修正图模型学习中的错误
On Different Notions of Redundancy in Conditional-Independence-Based Discovery of Graphical Models
- 通过未使用过的独立性检验探测模型缺陷
- 仅基于图结构假设的检验才具纠错潜力
- 适用于需高可靠性的因果建模场景
基于条件独立性的图模型发现方法依赖统计检验来识别变量间的独立结构。然而，这些检验可能不可靠，且算法对误差和假设违背敏感。实践中常存在未被用于构建图模型的冗余检验。本文表明，这些冗余检验具有检测甚至纠正学习模型错误的潜力。但进一步指出，并非所有检验都包含此类信息；只有那些仅由图结构假设推导出的条件（不）独立关系才能有效纠错，而对所有概率分布都成立的关系则难以发挥作用。因此，冗余检验的应用需谨慎。
原文摘要 · Abstract (English)
Conditional-independence-based discovery uses statistical tests to identify a graphical model that represents the independence structure of variables in a dataset. These tests, however, can be unreliable, and algorithms are sensitive to errors and violated assumptions. Often, there are tests that were not used in the construction of the graph. In this work, we show that these redundant tests have the potential to detect or sometimes correct errors in the learned model. But we further show that not all tests contain this additional information and that such redundant tests have to be applied with care. Precisely, we argue that the conditional (in)dependence statements that hold for every probability distribution are unlikely to detect and correct errors - in contrast to those that follow only from graphical assumptions.
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