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Learning General Causal Structures with Hidden Dynamic Process for Climate Analysis

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Climate Analysis; Causal Discovery; Causal Representation Learning
Abstract:

Understanding climate dynamics requires going beyond correlations in observational data to uncover their underlying causal process. Latent drivers, such as atmospheric processes, play a critical role in temporal dynamics, while direct causal influences also exist among geographically proximate observed variables. Traditional Causal Representation Learning (CRL) typically focuses on latent factors but overlooks such observable-to-observable causal relations, limiting its applicability to climate analysis. In this paper, we introduce a unified framework that jointly uncovers (i) causal relations among observed variables and (ii) latent driving forces together with their interactions. We establish conditions under which both the hidden dynamic processes and the causal structure among observed variables are simultaneously identifiable from time-series data. Remarkably, our guarantees hold even in the nonparametric setting, leveraging contextual information to recover latent variables and observable relations. Building on these insights, we propose CaDRe (Causal Discovery and Representation learning), a time-series generative model with structural constraints that integrates CRL and causal discovery. Experiments on synthetic datasets validate our theoretical results. On real-world climate datasets, CaDRe not only delivers competitive forecasting accuracy but also recovers visualized causal graphs aligned with domain expertise, thereby offering interpretable insights into climate systems.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper proposes a unified framework for jointly uncovering causal relations among observed climate variables and latent atmospheric driving forces from time-series data. It resides in the 'Joint Observable-Latent Causal Discovery' leaf of the taxonomy, which contains only two papers total. This is a notably sparse research direction within the broader field of learning causal structures and latent dynamics from time-series, suggesting the problem formulation—simultaneously identifying both observable causal graphs and hidden processes—remains relatively underexplored compared to methods focusing exclusively on either observable discovery or latent recovery.

The taxonomy reveals several neighboring research directions that provide context. The sibling leaf 'Temporally Causal Latent Process Recovery' focuses on identifying time-delayed latent variables without jointly modeling observable-to-observable causality, while 'Nonparametric and Dynamic Latent Models' addresses temporal evolution of latent factors. Nearby branches include 'Neural and Deep Learning-Based Discovery' (which emphasizes Granger causality and amortized inference) and 'Constraint-Based and Score-Based Approaches' (handling autocorrelation and confounders). The paper's emphasis on nonparametric identifiability and contextual information distinguishes it from purely neural or constraint-based methods, positioning it at the intersection of representation learning and structural discovery.

Among thirty candidates examined, the contribution-level analysis shows mixed novelty signals. The unified framework for joint discovery (Contribution 1) examined ten candidates with zero refutations, suggesting this specific integration may be relatively fresh. However, the nonparametric identifiability theory (Contribution 2) encountered one refutable candidate among ten examined, indicating some overlap with existing theoretical work on latent variable identification. The CaDRe generative model (Contribution 3) showed no refutations across ten candidates. These statistics reflect a limited search scope—top-K semantic matches plus citation expansion—rather than exhaustive coverage, so the absence of refutations does not guarantee absolute novelty.

Given the sparse taxonomy leaf and limited search scope, the work appears to occupy a relatively underexplored niche within causal time-series analysis. The joint observable-latent formulation and nonparametric guarantees represent substantive contributions, though the identifiability theory shows some prior overlap. The analysis covers approximately thirty semantically related papers, leaving open the possibility of additional relevant work in adjacent subfields or application domains not captured by the search strategy.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
30
Contribution Candidate Papers Compared
1
Refutable Paper

Research Landscape Overview

Core task: learning causal structures and latent dynamics from time-series data. The field organizes around several complementary branches. Causal Discovery Methods for Time-Series focuses on identifying directed relationships and temporal dependencies directly from observed sequences, often leveraging Granger-style reasoning or constraint-based approaches. Latent Variable and Representation Learning tackles scenarios where key drivers are unobserved, aiming to disentangle hidden factors and their causal roles—works like Learning temporally causal latent[6] and Discovering common hidden causes[7] exemplify this direction. Intervention-Based and Counterfactual Methods emphasize experimental or quasi-experimental designs to validate causal claims, while Domain-Specific Applications and Extensions adapt these techniques to fields such as neuroscience, climate science, and finance. Methodological Foundations and Surveys provide theoretical grounding and comparative analyses across these branches, helping researchers navigate trade-offs between identifiability, scalability, and interpretability. A particularly active line of work explores joint observable-latent causal discovery, where methods must simultaneously infer both the structure among measured variables and the influence of hidden confounders or latent processes. Learning General Causal Structures[0] sits squarely in this cluster, addressing the challenge of recovering causal graphs when latent dynamics are present. It shares thematic ground with Identification of Nonparametric Dynamic[11], which also considers nonparametric settings with unobserved variables, and contrasts with purely observable-variable approaches like DyCAST[2] or Causal Discovery in Temporal[16]. A central open question is how to balance model expressiveness—allowing flexible nonlinear or time-varying mechanisms—with the need for identifiability guarantees, especially when interventions are unavailable. Learning General Causal Structures[0] contributes to this debate by proposing conditions under which latent causal structures remain recoverable, positioning itself among efforts to extend classical causal discovery into richer, more realistic temporal settings.

Claimed Contributions

Unified framework for joint causal discovery and latent representation learning

The authors propose a framework that simultaneously identifies causal relationships among observed climate variables and recovers hidden latent processes along with their temporal dynamics, addressing limitations of traditional methods that focus on only one aspect.

10 retrieved papers
Nonparametric identifiability theory for latent space and causal structures

The authors develop theoretical identifiability conditions that guarantee recovery of both latent variables and causal graphs in a nonparametric setting, leveraging contextual information from time-series data without requiring parametric assumptions.

10 retrieved papers
Can Refute
CaDRe: time-series generative model with structural constraints

The authors introduce CaDRe, a practical implementation based on variational autoencoders with flow-based priors and gradient-based structural penalties that performs both causal representation learning and causal discovery simultaneously on time-series data.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Unified framework for joint causal discovery and latent representation learning

The authors propose a framework that simultaneously identifies causal relationships among observed climate variables and recovers hidden latent processes along with their temporal dynamics, addressing limitations of traditional methods that focus on only one aspect.

Contribution

Nonparametric identifiability theory for latent space and causal structures

The authors develop theoretical identifiability conditions that guarantee recovery of both latent variables and causal graphs in a nonparametric setting, leveraging contextual information from time-series data without requiring parametric assumptions.

Contribution

CaDRe: time-series generative model with structural constraints

The authors introduce CaDRe, a practical implementation based on variational autoencoders with flow-based priors and gradient-based structural penalties that performs both causal representation learning and causal discovery simultaneously on time-series data.

Table of Contents

Learning General Causal Structures with Hidden Dynamic Process for Climate Analysis | Novelty Validation