提出鲁棒方法分析含隐变量反馈系统的因果关系
Robust Causal Analysis of Linear Cyclic Systems With Hidden Confounders
- 基于理论分析改进现有方法，增强对循环因果系统的鲁棒性
- 新方法在存在隐藏混杂因素和数据污染时仍能准确识别因果结构
- 适合需要处理复杂反馈系统与不完整数据的研究者使用
我们生活在一个充满复杂系统的世界，仅靠概率分析不足以理解其本质，必须深入挖掘系统背后的机制。因果推断正是为此而生。许多复杂系统包含反馈回路，要求方法支持循环因果关系；同时系统通常无法完全隔离，存在隐藏混杂因子——即未观测到的变量同时影响多个可观测变量。此外，数据常受噪声或异常过程干扰，需具备鲁棒性。因此，本文研究了已有少数可处理循环模型与隐藏混杂因子的因果分析方法LLC（Linear Cyclic Causal model）的鲁棒性，并提出其鲁棒扩展。通过理论分析，验证了该方法在非理想数据下的稳定性。为促进可复现性与后续研究，代码已公开。
原文摘要 · Abstract (English)
We live in a world full of complex systems which we need to improve our understanding of. To accomplish this, purely probabilistic investigations are often not enough. They are only the first step and must be followed by learning the system's underlying mechanisms. This is what the discipline of causality is concerned with. Many of those complex systems contain feedback loops which means that our methods have to allow for cyclic causal relations. Furthermore, systems are rarely sufficiently isolated, which means that there are usually hidden confounders, i.e., unmeasured variables that each causally affects more than one measured variable. Finally, data is often distorted by contaminating processes, and we need to apply methods that are robust against such distortions. That's why we consider the robustness of LLC, see \cite{llc}, one of the few causal analysis methods that can deal with cyclic models with hidden confounders. Following a theoretical analysis of LLC's robustness properties, we also provide robust extensions of LLC. To facilitate reproducibility and further research in this field, we make the source code publicly available.
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