提出无需假设无环的因果效应估计方法，可直接用于有反馈的复杂系统。
Data-Driven Covariate Selection for Nonparametric and Cycle-Agnostic Causal Effect Estimation
- 基于条件独立性信息进行局部数据驱动的协变量选择
- 在有环和无环模型中均保持准确性和可靠性
- 适合处理含隐藏混杂与反馈回路的现实观测数据
从观测数据中估计因果效应需识别有效的调整集。在存在潜在混杂和反馈回路的现实场景中，该任务尤为困难。现有方法通常假设无环结构或依赖全局因果结构学习，限制了适用性与计算效率。本文研究一种基于条件独立信息的局部、数据驱动协变量选择方法。尽管该方法在无环模型中已被证明是完备且正确的，其在有环模型中的有效性尚不明确。我们的主要贡献是证明这些保证在有环因果模型中依然成立。关键在于条件独立性断言在σ-无环化下的不变性。这一结果建立了统一的、与环无关的协变量选择视角，表明该方法无需修改即可适用于有环与无环场景。我们在大量合成数据上验证了该方法在有环模型中的稳健表现。
原文摘要 · Abstract (English)
Estimating causal effects from observational data requires identifying valid adjustment sets. This task is especially challenging in realistic settings where latent confounding and feedback loops are present. Existing approaches typically assume acyclicity or rely on global causal structure learning, limiting applicability and computational efficiency. In this work, we study a local, data-driven method for covariate selection based on conditional independence information. While this method is known to be sound and complete in acyclic causal models, its validity in the presence of cycles has remained unclear. Our main contribution is to show that these guarantees extend to cyclic causal models. In particular, our result relies on the invariance of conditional independence assertions under $σ$-acyclification. These findings establish a unified, cycle-agnostic perspective on covariate selection and causal effect estimation, showing that the method applies across cyclic and acyclic settings without modification. Empirically, we validate this on extensive synthetic data, showing reliable performance in cyclic causal models.
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