提出基于因果效应估计的图距离度量，解决隐变量下图结构评估难题。
Graph Distance Based on Cause-Effect Estimands with Latents
- 通过固定和符号验证量化图差异对因果效应的影响。
- 在不同图扰动下验证度量稳定性，优于现有指标。
- 适合评估含隐变量的因果发现方法性能。
因果发现旨在从观测数据中恢复变量间的因果关系图，但有效评估发现的图仍具挑战性，尤其在存在隐变量混杂时。本文提出一种基于下游因果效应估计任务的无环有向混合图（ADMG）图距离度量，利用识别方法中的固定机制与符号验证器，量化不同图结构差异对各类处理-结果对因果效应估计量的影响。我们分析了该度量在多种图扰动下的表现，并与现有距离度量进行比较，验证其有效性与鲁棒性。
原文摘要 · Abstract (English)
Causal discovery aims to recover graphs that represent causal relations among given variables from observations, and new methods are constantly being proposed. Increasingly, the community raises questions about how much progress is made, because properly evaluating discovered graphs remains notoriously difficult, particularly under latent confounding. We propose a graph distance measure for acyclic directed mixed graphs (ADMGs) based on the downstream task of cause-effect estimation under unobserved confounding. Our approach uses identification via fixing and a symbolic verifier to quantify how graph differences distort cause-effect estimands for different treatment-outcome pairs. We analyze the behavior of the measure under different graph perturbations and compare it against existing distance metrics.
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