论文标题
在存在周期的情况下，使用部分祖先图的基于约束的因果发现
Constraint-Based Causal Discovery using Partial Ancestral Graphs in the presence of Cycles
论文作者
论文摘要
虽然已知反馈回路在许多复杂的系统中起着重要作用，但在因果发现文献的很大一部分中，它们的存在被忽略了，因为通常从一开始就假定系统是无环的。在应用涉及反馈的系统生成的数据上，应用为无环设置设计的因果发现算法时，人们不会期望获得正确的结果。在这项工作中，我们表明 - 令人惊讶的是，如果将其应用于涉及反馈的系统生成的观察数据，那么快速因果推理（FCI）算法的输出是正确的。更具体地说，我们证明，对于由简单且$σ$ - 信仰的结构性因果模型（SCM）产生的观察数据，FCI是合理而完整的，可用于始终如一地估计（i）存在因果关系，（ii）直接的因果关系，（iii）缺乏（iii），（iii ii）（iii）（III）（III）（III IIV）（IIV）（iiv） SCM。我们将这些结果扩展到基于约束的因果发现算法，这些算法利用了某些形式的背景知识，包括因果关系足够的设置（例如，PC算法）和关节因果推理设置（例如FCI-JCI算法）。
While feedback loops are known to play important roles in many complex systems, their existence is ignored in a large part of the causal discovery literature, as systems are typically assumed to be acyclic from the outset. When applying causal discovery algorithms designed for the acyclic setting on data generated by a system that involves feedback, one would not expect to obtain correct results. In this work, we show that -- surprisingly -- the output of the Fast Causal Inference (FCI) algorithm is correct if it is applied to observational data generated by a system that involves feedback. More specifically, we prove that for observational data generated by a simple and $σ$-faithful Structural Causal Model (SCM), FCI is sound and complete, and can be used to consistently estimate (i) the presence and absence of causal relations, (ii) the presence and absence of direct causal relations, (iii) the absence of confounders, and (iv) the absence of specific cycles in the causal graph of the SCM. We extend these results to constraint-based causal discovery algorithms that exploit certain forms of background knowledge, including the causally sufficient setting (e.g., the PC algorithm) and the Joint Causal Inference setting (e.g., the FCI-JCI algorithm).
