Robust Causal Directionality Inference in Quantum Inference under MNAR Observation and High-Dimensional Noise
Published:Dec 24, 2025 05:00
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Analysis
This arXiv paper presents a novel framework for inferring causal directionality in quantum systems, specifically addressing the challenges posed by Missing Not At Random (MNAR) observations and high-dimensional noise. The integration of various statistical techniques, including CVAE, MNAR-aware selection models, GEE-stabilized regression, penalized empirical likelihood, and Bayesian optimization, is a significant contribution. The paper claims theoretical guarantees for robustness and oracle inequalities, which are crucial for the reliability of the method. The empirical validation using simulations and real-world data (TCGA) further strengthens the findings. However, the complexity of the framework might limit its accessibility to researchers without a strong background in statistics and quantum mechanics. Further clarification on the computational cost and scalability would be beneficial.
Key Takeaways
- •Introduces a novel framework for causal directionality inference in quantum systems under MNAR observation.
- •Integrates CVAE, MNAR-aware selection models, GEE-stabilized regression, penalized empirical likelihood, and Bayesian optimization.
- •Claims theoretical guarantees for double robustness, perturbation stability, and oracle inequalities.
Reference
“This establishes robust causal directionality inference as a key methodological advance for reliable quantum engineering.”