MissNODAG: Differentiable Cyclic Causal Graph Learning from Incomplete Data

2024/10/24 by Muralikrishnna G. Sethuraman, Sethuraman, Muralikrishnna G., Razieh Nabi +3
Computer Science · Decision Sciences · #Advanced Graph Neural Networks #Bayesian Modeling and Causal Inference #Data Quality and Management #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)

paper · pdf · doi:10.48550/arxiv.2410.18918

openalex publication_date 2024/10/24 · openalex created_date 2024/11/13 · openalex updated_date 2026/07/28

Abstract

Causal discovery in real-world systems, such as biological networks, is often complicated by feedback loops and incomplete data. Standard algorithms, which assume acyclic structures or fully observed data, struggle with these challenges. To address this gap, we propose MissNODAG, a differentiable framework for learning both the underlying cyclic causal graph and the missingness mechanism from partially observed data, including data missing not at random. Our framework integrates an additive noise model with an expectation-maximization procedure, alternating between imputing missing values and optimizing the observed data likelihood, to uncover both the cyclic structures and the missingness mechanism. We establish consistency guarantees under exact maximization of the score function in the large sample setting. Finally, we demonstrate the effectiveness of MissNODAG through synthetic experiments and an application to real-world gene perturbation data.

Related

• Learning Cyclic Causal Models from Incomplete Data
• Differentiable Cyclic Causal Discovery Under Unmeasured Confounders
• MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models
• Causal Discovery from Incomplete Data using An Encoder and Reinforcement Learning
• Causal Discovery from Incomplete Data: A Deep Learning Approach