Title: UnCLe: Towards Scalable Dynamic Causal Discovery in Non-linear Temporal Systems
Venue: NeurIPS 2025
Accepted: yes
Keywords: causal discovery

dynamic causality

time series

representation learning
Abstract: Uncovering cause-effect relationships from observational time series is fundamental to understanding complex systems. While many methods infer static causal graphs, real-world systems often exhibit *dynamic causality*—where relationships evolve over time. Accurately capturing these temporal dynamics requires time-resolved causal graphs. We propose UnCLe, a novel deep learning method for scalable dynamic causal discovery. UnCLe employs a pair of Uncoupler and Recoupler networks to disentangle input time series into semantic representations and learns inter-variable dependencies via auto-regressive Dependency Matrices. It estimates dynamic causal influences by analyzing datapoint-wise prediction errors induced by temporal perturbations. Extensive experiments demonstrate that UnCLe not only outperforms state-of-the-art baselines on static causal discovery benchmarks but, more importantly, exhibits a unique capability to accurately capture and represent evolving temporal causality in both synthetic and real-world dynamic systems (e.g., human motion). UnCLe offers a promising approach for revealing the underlying, time-varying mechanisms of complex phenomena.


Peer reviews:
comments: SUMMARY:
This paper proposes the UnCLe algorithm, a Granger causal discovery method that can extract dynamic causal structures. The UnCLe algorithm first uses TCN to encode the original variable into a latent representation, and then searches for a dynamic causal dependency matrix in the latent space. It should be noted that a reconstruction loss supervises the latent representation.

STRENGTHS AND WEAKNESSES:
**Strengths**

1. This method proposes a dynamic inter-slice causal structure algorithm based on Granger causality, which better matches the real dynamic system.

2. This method can obtain the causal dependencies of dynamic and static structures at the same time.

**Weaknesses**

1. Eq. (3) and Eq. (4) are Granger causal models that model lag 1, and it seems that the possibility of extending the model to higher orders is not explored.

2. The author provides a reasonable modeling method and mathematical expressions, but there is no theoretical analysis of the identifiability of cause and effect, especially since this article will learn causal relationships in the latent space.

QUESTIONS:
1. How can we ensure the identifiability of the causal dependency structure discovered by the proposed method in latent space? I will be happy to improve the score if this issue is solved.

2. The dynamic causal dependency matrix is ​​defined as $C$ learnable $N \times N$ matrices. This may cause problems when dealing with high-dimensional time series, as it significantly increases the number of parameters that need to be learned.

3. The learning strategy of Ψ seems to need further explanation. Is it a randomly initialized matrix? Is there any additional design?

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-20

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces UnCLe (UnCoupLing causality), a novel deep learning-based framework for scalable, dynamic causal discovery in multivariate time series data. Unlike existing methods that primarily produce static or time-aggregated causal graphs, UnCLe explicitly models evolving temporal causality using a two-part architecture: (1) Uncoupler and Recoupler networks that disentangle input signals into semantic latent representations, and (2) auto-regressive Dependency Matrices to capture inter-variable dependencies. Dynamic causal influence is inferred post hoc by perturbing individual time series and measuring changes in prediction error, thus revealing time-specific causal structures. The authors support their claims with extensive experiments on synthetic, semi-synthetic, and real-world datasets, including human motion capture, and demonstrate that UnCLe outperforms state-of-the-art baselines in both accuracy and scalability.\n\nSTRENGTHS AND WEAKNESSES:\nThe paper's principal strength lies in its clear identification of a significant gap in current causal discovery literature—namely, the lack of effective, scalable tools for modeling dynamic causal structures. UnCLe is a relevant contribution: its perturbation-based post-hoc analysis offers a novel approach to quantifying time-varying causal influence, and the architectural design with shared TCN-based encoders/decoders enhances scalability and robustness. The results demonstrate UnCLe’s effectiveness, especially in dynamic scenarios where it outperforms the closest dynamic baseline, GVAR, both qualitatively (e.g., motion phase interpretation) and quantitatively. Overall, the paper is well-written. Although Python code and datasets are provided in the supplementary material, I did not verify the reproducibility of the experiments.\n\nRegarding weaknesses, while the model architecture is novel in its integration of existing components (e.g., TCNs, perturbation-based attribution), it does not introduce fundamentally new neural paradigms. Furthermore, the paper could engage more deeply with theoretical guarantees (e.g., identifiability, spurious effects), and the interpretability of semantic channels. Lastly, the dynamic causal graphs are evaluated largely through visual or intuitive means (e.g., MoCap phases). More rigorous quantitative evaluation on dynamic benchmarks would further strengthen the case.\n\nQUESTIONS:\nCan the authors clarify how the Dependency Matrices Ψ relate to true causal effects across different channels?\n\nThe causal strength estimation relies on random permutations of the input time series. How sensitive is UnCLe’s performance to the specific choice of perturbation strategy?\n\nSince the method estimates causality by measuring increases in prediction error under perturbation, how does it guard against attributing causality to spurious correlations, especially in high-dimensional settings with limited data?\n\nWhile UnCLe scales better than several baselines, can the authors provide more detailed runtime and memory usage analysis? This would help in assessing suitability for very high-dimensional settings.\n\nLIMITATIONS:\nLimitations and potential egative impact are adequately discussed.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-25"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nUnCLe is a scalable framework for causal discovery in non-linear, high-dimensional time series. A parameter-sharing TCN encoder learns latent features, while a sparse auto-regressive matrix Ψ predicts the next step. Causal graphs are then extracted in two ways:\n1. Dynamic – randomly permuting one series and measuring the error increase on others (fast, perturbation-based).\n2. Static – aggregating Ψ weights without extra passes.\nAcross synthetic static/dynamic benchmarks, fMRI, human motion capture, and 300-node traffic data, UnCLe matches or outperforms nine baselines, sits on the AUROC / runtime Pareto frontier, and yields interpretable graphs—yet keeps parameters and runtime modest thanks to weight sharing.\n\nAcross synthetic static/dynamic benchmarks, fMRI, human motion capture, and 300-node traffic data, UnCLe matches or outperforms nine baselines, sits on the AUROC / runtime Pareto frontier, and yields interpretable graphs—yet keeps parameters and runtime modest thanks to weight sharing.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. Novel deep learning method explicitly targeting time-resolved causal graphs in large, non-linear systems.\n2. Perturbation yields accurate dynamic edges; aggregation gives an immediate static summary without extra passes—useful in latency-constrained settings.\n3. Extensive evaluation on synthetic (static and dynamic), medium-dimensional medical data, high-dimensional traffic & biomechanics; baselines span classical, score-based, and neural Granger families.\n\nWeaknesses:\n1. No identifiability guarantee: the method relies on predictive perturbations but does not analyse conditions (e.g., faithfulness, invertibility) under which the framework recovers the true causal strength.\n2. Ψ operates only on lag 1. Although the authors use dilated TCNs, empirical degradation when true lags > 1 is not studied.\n3. Error gains depend on permutation; robustness to alternative perturbations (masking, noise injection) is unclear.\n\nQUESTIONS:\n1. How did the framework perform on time series data with missingness or irregularities?\n2. Can this post-hoc perturbation method be extended to other similar temporal deep learning frameworks?\n3. L1 on Ψ encourages sparsity, but dynamic graphs in nature may densify temporarily; does the regulariser hamper detection of dense episodes?\n\nLIMITATIONS:\nThe paper’s limitations section is reasonably clear about scalability ceilings and the lack of formal identifiability guarantees, but the broader-impact discussion is essentially missing (the checklist marks “N/A”). I recommend the authors:\n\n1. Add a short societal-impact paragraph: spell out potential misuse risks (e.g., drawing unjustified causal claims in sensitive domains like finance, health, or policing) and emphasize that UnCLe’s graphs should not be used for high-stakes decisions without expert validation.\n2. Clarify deployment safeguards: outline recommendations such as thresholding edges by confidence, combining UnCLe with domain knowledge, or performing prospective validation before operational use.\n\nIncorporating these points would more fully address the negative societal impact while rewarding the authors’ transparency.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe article proposes UnCLe, a method for dynamic causal discovery. The method using an auto-encoding type structure to learn a latent representation of a nonlinear time series which linearizes the underlying dynamics. The method is evaluated on several interesting synthetic and real-world examples. Experimental results demonstrate the method effectively and suggest it to be an important contribution to the field.\n\nSTRENGTHS AND WEAKNESSES:\nThe article is clearly written and easy to follow. The proposed method is interesting and well-detailed. The authors provide compelling motivation, and the experiments clearly support the proposed method.\n\nI only identified minor weaknesses in the article, which I have detailed below. \n- UnCLe(P) appears to be uniformly better than UnCLe(A). The authors state that UnCLe(A) might be preferable when speed is desired for causal discovery. But when would I want speed? Causal discovery is often a scientific task, performed offline on pre-recorded data sets, and it's hard for me to imagine when I would need to speed up this procedure. Also, it's not clear that the gain in time that I get from using UnCLe(A) vs UnCLe(P) is significant relative to the overhead of training the Uncoupler\\Recoupler networks. Figure 4 suggests that running UnCLe already takes a few minutes. Does this speed up significantly if I use (A)? Please comment on this. \n- The experiment with the dynamic SEM would benefit from a figure that compares the \"ground truth\" causal strength to the UnCLe causal strengths, and to comment on whether they align or not, and why the result is\\isn't reasonable.\n- Which version of UnCLe is used in Figure 4? \n- The discussion surrounding the MoCap experiment would benefit from adding references to existing literature that explains how the UnCLe method produced a \"better\" time-varying graph. Without a ground truth, asserting that the UnCLe graph is preferrable requires the reader to have some kind of knowledge of MoCap. In the worst case, it might be true that dense graphs are to be expected in MoCap and that the sparsity of the UnCLe graphs is undesirable. Please comment on this.\n\nQUESTIONS:\n- Why are the static and dynamic graphs learned by apparently two distinct mechanisms? Can the authors comment on why they proposed two distinct methods in this article?\n\n- How do we verify linearity in latent dynamics is a good assumption? Linear dynamics are NOT sufficient to describe nonlinear phenomena. That said, linear models can be useful to represent nonlinear dynamics when the correct representation is used (Brunton et al, 2017). In that sense, UnCLe could argued for quite nicely using existing theory. I recommend that the authors take a look at some of the literature on the linearization of nonlinear dynamic systems (e.g., (Socha, 2007)) to understand the limitations of their proposed method, and to consider these ideas in future work.\n\n- It should be emphasized in the article that the reason that the UnCLe(P) method can detect causation from prediction is specifically because it models the causal mechanism that generates the data. Hence, if the predictions are inaccurate, it indicates that the assumed causal dynamics did not anticipate the next state, which is one consequence of the graph changing over time. \n\n### References \n\nBrunton, Steven L., et al. \"Chaos as an intermittently forced linear system.\" Nature communications 8.1 (2017): 19.\n\nSocha, Leslaw. Linearization methods for stochastic dynamic systems. Vol. 730. Springer, 2007.\n\nLIMITATIONS:\nI believe the authors commented sufficiently on the limitations of the work, apart from my requests above.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}