提出新方法在未知干预目标下发现非线性循环因果关系
SCOUT: Cyclic Causal Discovery Under Soft Interventions with Unknown Targets
- 用可逆流模型最大化数据似然，推断循环因果结构
- 在合成与真实数据上均优于现有方法，尤其在未知干预目标时
- 适合处理复杂现实系统中的非线性、循环、软干预数据
从数据中学习变量间的因果关系是跨学科的基础研究。现有方法通常依赖三个假设：(i) 系统无环，(ii) 外生噪声为高斯分布，(iii) 干预目标已知。这些假设虽简化分析，但常不成立。多数现有方法要么假设模型为线性，要么受限于特定干预场景。为此，我们提出SCOUT，一种从软干预数据中学习非线性循环因果关系的新框架。该方法通过最大化数据对数似然来恢复图结构，采用两种归一化流架构：收缩残差流与神经样条流。在合成与真实数据上的实验表明，SCOUT在因果图恢复与未知目标识别方面均优于当前最优方法，适用于多种干预与噪声设置。
原文摘要 · Abstract (English)
Learning causal relationships between variables from data is a fundamental research area with many applications across disciplines. Most existing causal discovery algorithms rely on the assumptions that (i) the underlying system is acyclic, (ii) the exogenous noise variables are Gaussian, and (iii) the intervention targets for the data-generating experiments are known. While these assumptions simplify the analysis, they are violated in real-life systems. Most existing methods that address these issues either assume the underlying model is linear or are constrained to operate in limited interventional settings. To that end, we propose SCOUT, a novel causal discovery framework for learning nonlinear cyclic causal relationships from soft interventional data with unknown targets. Our approach maximizes the data log-likelihood to recover the graph structure, using two normalizing-flow architectures: contractive residual flows and neural spline flows. Through experiments on synthetic and real-world data, we show that SCOUT outperforms state-of-the-art methods in both causal graph recovery and unknown target recovery across various interventional and noise settings.
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