iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models
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iSCAN：识别非线性加性噪声模型中的因果机制变化
- 研究问题
- 在变量相同的多个相关数据集中，能否不先估计各自完整因果图，就定位发生变化的因果机制及相关结构差异？
- 方法与思路
- 作者在非线性加性噪声模型中，利用混合分布对数密度得分函数的雅可比矩阵识别变化，再估计相关变量的结构差异。
- 材料陈述的贡献
- 摘要称，该技术支持一般非参数函数机制下的变化识别，扩展已有线性高斯设定，并提供合成、真实数据实验及开源代码。
- 阅读关注点
- 应核对机制变化的定义、混合分布的构造、雅可比矩阵为何支持识别，以及估计误差和结构差异恢复的条件。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未经核实；摘要未列明识别条件，不能推断其适用于任意非线性因果模型。
原文摘要
Structural causal models (SCMs) are widely used in various disciplines to represent causal relationships among variables in complex systems. Unfortunately, the underlying causal structure is often unknown, and estimating it from data remains a challenging task. In many situations, however, the end goal is to localize the changes (shifts) in the causal mechanisms between related datasets instead of learning the full causal structure of the individual datasets. Some applications include root cause analysis, analyzing gene regulatory network structure changes between healthy and cancerous individuals, or explaining distribution shifts. This paper focuses on identifying the causal mechanism shifts in two or more related datasets over the same set of variables -- without estimating the entire DAG structure of each SCM. Prior work under this setting assumed linear models with Gaussian noises; instead, in this work we assume that each SCM belongs to the more general class of nonlinear additive noise models (ANMs). A key technical contribution of this work is to show that the Jacobian of the score function for the mixture distribution allows for the identification of shifts under general non-parametric functional mechanisms. Once the shifted variables are identified, we leverage recent work to estimate the structural differences, if any, for the shifted variables. Experiments on synthetic and real-world data are provided to showcase the applicability of this approach. Code implementing the proposed method is open-source and publicly available at https://github.com/kevinsbello/iSCAN.