提出新方法直接找因果顺序，无需复杂优化和先验知识。
Causal Order Discovery based on Monotonic SCMs
- 通过迭代检测根变量来发现因果顺序
- 无需稀疏性假设，避免复杂优化问题
- 适合需要简化因果推断流程的研究者
本文研究单调结构因果模型（Monotonic SCMs）框架下的因果顺序发现问题。该框架因能从观测数据中实现因果推断与发现而受到关注。现有方法或依赖因果顺序的先验知识，或使用复杂的优化技术来强制三角单调递增映射的雅可比矩阵稀疏。本文提出一种新颖的序列化方法，通过迭代检测根变量直接识别因果顺序，无需稀疏性假设及相应优化挑战，从而在不进行多次独立性检验的前提下唯一确定一个结构因果模型。实验表明，该方法在逐次寻找根变量方面优于最大化雅可比稀疏性的方法。
原文摘要 · Abstract (English)
In this paper, we consider the problem of causal order discovery within the framework of monotonic Structural Causal Models (SCMs), which have gained attention for their potential to enable causal inference and causal discovery from observational data. While existing approaches either assume prior knowledge about the causal order or use complex optimization techniques to impose sparsity in the Jacobian of Triangular Monotonic Increasing maps, our work introduces a novel sequential procedure that directly identifies the causal order by iteratively detecting the root variable. This method eliminates the need for sparsity assumptions and the associated optimization challenges, enabling the identification of a unique SCM without the need for multiple independence tests to break the Markov equivalence class. We demonstrate the effectiveness of our approach in sequentially finding the root variable, comparing it to methods that maximize Jacobian sparsity.
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