在量子观测中解决因果方向推断难题，提升可靠性。
Robust Causal Directionality Inference in Quantum Inference under MNAR Observation and High-Dimensional Noise
- 融合变分自编码与选择模型，处理非随机缺失数据
- 实测数据中偏差更小，覆盖概率接近理论值
- 适合量子工程与高维生物数据的因果分析
在量子力学中，观测本身会主动影响系统，这类似于统计学中的非随机缺失（MNAR）问题。本文提出统一框架，用于量子工程中的鲁棒因果方向推断，判断关系是系统→观测、观测→系统，还是双向。方法结合基于条件变分自编码器（CVAE）的潜在约束、面向MNAR的选择模型、广义估计方程（GEE）稳定回归、惩罚似然估计（PEL）及贝叶斯优化，联合处理量子与经典噪声，揭示因果方向。理论证明具有双重稳健性、扰动稳定性及极小化误差界。模拟与真实数据实验（TCGA基因表达、蛋白质组数据）表明，所提的MNAR稳定化CVAE+GEE+AIPW+PEL框架显著降低偏差与方差，实现近名义覆盖率，并具备优异的量子特异性诊断能力。该工作确立了鲁棒因果方向推断为可靠量子工程的关键方法进展。
原文摘要 · Abstract (English)
In quantum mechanics, observation actively shapes the system, paralleling the statistical notion of Missing Not At Random (MNAR). This study introduces a unified framework for \textbf{robust causal directionality inference} in quantum engineering, determining whether relations are system$\to$observation, observation$\to$system, or bidirectional. The method integrates CVAE-based latent constraints, MNAR-aware selection models, GEE-stabilized regression, penalized empirical likelihood, and Bayesian optimization. It jointly addresses quantum and classical noise while uncovering causal directionality, with theoretical guarantees for double robustness, perturbation stability, and oracle inequalities. Simulation and real-data analyses (TCGA gene expression, proteomics) show that the proposed MNAR-stabilized CVAE+GEE+AIPW+PEL framework achieves lower bias and variance, near-nominal coverage, and superior quantum-specific diagnostics. This establishes robust causal directionality inference as a key methodological advance for reliable quantum engineering.
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