The DeCAMFounder: Non-Linear Causal Discovery in the Presence of Hidden Variables
DeCAMFounder：隐藏变量下的非线性因果发现AI 解读 ↓
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DeCAMFounder：隐藏变量下的非线性因果发现
- 研究问题
- 当观测数据同时受到非线性关系与广泛混杂影响时，能否利用混杂结构恢复变量之间的因果关系及因果图顺序？
- 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，并分析其恢复正确图顺序的一致性。
- 材料陈述的贡献
- 摘要称将广泛混杂下的因果发现推进至非线性情形，证明相关一致性，并在模拟和真实数据上展示性能改善。
- 阅读关注点
- 应核对广泛混杂的精确定义、谱分解可分离混杂的条件，以及正确排序与完整图恢复之间的区别和比较基准。
- 阅读边界
- 所据为 arXiv 预印本，同行评审或期刊接收未经核实；摘要未列非线性及混杂的完整限制，不能推及任意隐藏变量情形。
原文摘要
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 utilized for consistent causal estimation in linear causal models. In this paper, we present a provably consistent method to estimate causal relationships in the non-linear, 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 non-linear effects.