The DeCAMFounder: nonlinear causal discovery in the presence of hidden variables
DeCAMFounder：存在隐藏变量时的非线性因果发现AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确研究隐藏混杂下的非线性因果发现及因果图排序，直接对应因果推断。
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DeCAMFounder：存在隐藏变量时的非线性因果发现
- 研究问题
- 在观测数据受到广泛隐藏混杂且变量关系非线性时，能否利用混杂结构恢复因果关系，尤其是有向无环图的正确排序？
- 方法与思路
- 作者通过观测数据矩阵的谱分解估计混杂变动，据此构造有向无环图评分函数，并分析排序一致性及模拟和真实数据表现。
- 材料陈述的贡献
- 摘要称，所提方法在非线性、广泛混杂设定下具有一致性，能够恢复正确图排序，并在所用模拟与真实数据中改善表现。
- 阅读关注点
- 应核对广泛混杂与非线性模型的具体条件、谱分解误差如何进入评分，以及正确排序与完整因果图识别之间的区别。
- 阅读边界
- 仅据所给 Crossref 摘要；完整识别与一致性条件、比较指标及数据细节未说明，不能将正确排序保证扩展为任意隐藏混杂下的图恢复。
原文摘要
Abstract Many real-world decision-making tasks require learning causal relationships between a set of variables. Traditional causal discovery methods, however, require that all variables are observed, which is often not feasible in practical scenarios. Without additional assumptions about the unobserved variables, it is not possible to recover any causal relationships from observational data. Fortunately, in many applied settings, additional structure among the confounders can be expected. In particular, pervasive confounding is commonly encountered and has been utilised for consistent causal estimation in linear causal models. In this article, we present a provably consistent method to estimate causal relationships in the nonlinear, pervasive confounding setting. The core of our procedure relies on the ability to estimate the confounding variation through a simple spectral decomposition of the observed data matrix. We derive a DAG score function based on this insight, prove its consistency in recovering a correct ordering of the DAG, and empirically compare it to previous approaches. We demonstrate improved performance on both simulated and real datasets by explicitly accounting for both confounders and nonlinear effects.