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.

翻译：针对不完备数据进行因果发现的标准约束范式（先插值、后检验）常存在校准偏差：当插值误差诱发虚假条件依赖时，任何一致的条件独立性检验均会以趋近于1的概率拒绝真实原假设。我们提出PAIR-CI——一种通过配对排列设计将多重插值直接融入推断流程以恢复校准能力的非参数条件独立性检验。该方法在接收相同插值条件集的同时，对比纳入与排除候选变量的交叉验证模型，迫使插值误差在模型损失差中相互抵消，而非污染检验统计量。我们构建了一个经证明具有一致性的方差估计器，可联合考量交叉验证与多重插值产生的不确定性——据我们所知，这是首个正式统一这两种推断框架的方法。仿真实验表明，当数据为随机缺失时，现有基于插值的条件独立性检验假阳性率达28-45%，而PAIR-CI在不同数据生成过程与缺失机制下均值低于名义5%水平。该优势在非线性场景中最为显著，且随因果图规模扩大而增强：集成至PC算法后，PAIR-CI在10变量非线性图中将结构汉明距离降低8%，在30变量等效图中降低15%，在56变量的HAILFINDER网络中降幅达44%，且在所有场景中表现稳定。