解决缺失数据下因果发现的误判问题，提升测试准确性。
PAIR-CI: Calibrated Conditional Independence Testing for Causal Discovery with Incomplete Data
- 通过配对置换设计将多重插补融入检验过程，自动消除插补误差影响。
- 在非随机缺失数据中，错误拒绝率低于5%，远优于现有方法（28%-45%）。
- 适合处理高维非线性因果图，尤其在复杂网络中表现更稳定。
标准的基于约束的因果发现方法在缺失数据下常因插补引入虚假条件依赖而出现校准偏差：任何一致的条件独立（CI）检验在插补误差导致伪依赖时，对真实零假设的拒绝概率趋近于1。本文提出PAIR-CI，一种非参数化条件独立检验方法，通过配对置换设计将多重插补直接整合到推断流程中，恢复检验校准性。PAIR-CI在交叉验证模型中对比包含与不包含候选变量的情况，同时使用相同的插补条件集，使插补误差在损失差中相互抵消，而非污染检验统计量。其可证明一致的方差估计器联合考虑了交叉验证和多重插补带来的不确定性——据我们所知，这是首个正式统一这两种推断框架的方法。模拟实验显示，当数据缺失不随机（MNAR）时，现有插补式CI检验的假阳性率高达28%–45%，而PAIR-CI在各类生成过程和缺失机制下平均保持在名义5%以下。在非线性设定下增益最大，且随因果图规模增大：集成至PC算法后，10变量非线性图上结构汉明距离降低8%，30变量图上降低15%，56变量HAILFINDER网络上最高降低44%，且所有场景性能稳定。
原文摘要 · Abstract (English)
The standard constraint-based paradigm for causal discovery with incomplete data -- impute first, test second -- is frequently miscalibrated: any consistent conditional independence (CI) test rejects a true null with probability approaching 1 when imputation error induces spurious conditional dependence. We introduce PAIR-CI, a nonparametric CI test that restores calibration by integrating multiple imputation directly into the inferential procedure via a paired permutation design. PAIR-CI compares cross-validated models that include and exclude the candidate variable while receiving the same imputed conditioning set, forcing imputation error to cancel in their loss difference rather than contaminate the test statistic. A provably consistent variance estimator jointly accounts for uncertainty arising from cross-validation and multiple imputation -- to our knowledge, the first formal unification of these two inferential frameworks. In simulations, existing imputation-based CI tests exhibit false positive rates of 28--45% when data are missing not at random (MNAR), whereas PAIR-CI averages below the nominal 5% level across data-generating processes and missingness mechanisms. These gains are largest in nonlinear settings and grow with causal graph size: when integrated into the PC algorithm, PAIR-CI reduces structural Hamming distance by 8% on 10-variable nonlinear graphs, 15% on 30-variable equivalents, and up to 44% on the 56-variable HAILFINDER network, with stable performance in all settings.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。