DVAE: A Dynamic Variational Autoencoder for Structured Causal Discovery with Application in Biomedical Time Series
Bibliographic record
Abstract
Causal discovery in time-series data is critical for analyzing dynamic systems across neuroscience, economics, and biomedical signal processing. Traditional methods, such as Vector Auto-regression (VAR) and constraint-based approaches, struggle with high-dimensional dependencies, nonlinear relationships, and non-stationary dynamics. Deep learning-based models, including cMLP, cLSTM, and VAE-based approaches, aim to address these challenges but suffer from instability, over-pruning, and reliance on sparsity constraints. While cMLP provides lag-specific causal inference, its accuracy is limited, and other methods fail to explicitly capture lag-wise dependencies. This paper introduces DVAE-GC, a structured deep learning framework integrating dynamic variational inference with lag-structured recurrent MLPs (lsrMLP) to explicitly model time-lagged causal dependencies. Unlike prior methods that infer causality via weight sparsity, DVAE-GC progressively refines causal estimation, leveraging a bidirectional recurrent encoder and structured decoder. Additionally, Noise Invalidation Soft Thresholding (NIST) eliminates spurious connections, enhancing interpretability and robustness. Empirically, DVAE-GC outperforms the best baseline (CUTS) on VAR(9) by +18.3 absolute F1 points averaged over multiple noise levels, and on NetSim fMRI-20 by +8.1 absolute F1 points averaged over sequence multiple lengths; in simulated atrial rotor detection, it improves Rotational Activity Estimation Precision (RAEP) by +22.4 % over the best alternative (VAR). These are absolute-point gains, and also precision, recall, and false discovery rate (FDR) has been reported. Although evaluated in biomedical simulations, DVAE-GC applies broadly to time-series domains, including neuroscience, climate science, and financial modeling.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".