OpenNovelty.org

Beyond DAGs: A Latent Partial Causal Model for Multimodal Learning

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.5Download Report PDF
Multimodal Representation LearningLatent Variable ModelDisentangled Representation Learning
Abstract:

Directed Acyclic Graphs (DAGs) are a standard tool in causal modeling, but their suitability for capturing the complexity of large-scale multimodal data is questionable. In practice, real-world multimodal datasets are often collected from heterogeneous generative processes that do not conform to a single DAG. Instead, they may involve multiple, and even opposing, DAG structures with inverse causal directions. To address this gap, in this work, we first propose a novel latent partial causal model tailored for multimodal data representation learning, featuring two latent coupled variables parts connected by an undirected edge, to represent the transfer of knowledge across modalities. Under specific statistical assumptions, we establish an identifiability result, demonstrating that representations learned by MultiModal Contrastive Learning (MMCL) correspond to the latent coupled variables up to a trivial transformation. This result deepens our understanding of the why MMCL works, highlights its potential for representation disentanglement, and expands the utility of pre-trained models like CLIP. Synthetic experiments confirm the robustness of our findings, even when the assumptions are partially violated. Most importantly, experiments on a pre-trained CLIP model embodies disentangled representations, enabling few-shot learning and improving domain generalization across diverse real-world datasets. Together, these contributions push the boundaries of MMCL, both in theory and in practical applications.

Disclaimer
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.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper proposes a latent partial causal model for multimodal representation learning, featuring two latent coupled variables connected by an undirected edge to capture cross-modal knowledge transfer. It establishes identifiability results showing that multimodal contrastive learning (MMCL) recovers these latent variables up to trivial transformations. Within the taxonomy, this work resides in the 'Identifiability under Partial Observability' leaf, which contains only one sibling paper. This sparse population suggests the specific focus on partial causal structures with undirected coupling represents a relatively underexplored niche within the broader theoretical foundations branch.

The taxonomy reveals that the paper's immediate neighborhood—'Theoretical Foundations and Identifiability'—contains two other leaves: 'Contrastive Learning Theory' (analyzing MMCL through causal lenses) and 'Non-Markovian and Temporal Causal Systems' (addressing temporal dependencies). The paper bridges these areas by providing theoretical grounding for contrastive methods while remaining in the static, non-temporal regime. Adjacent branches like 'Causal Inference and Effect Estimation' focus on intervention estimation rather than representation identifiability, and 'Debiasing and Robustness' addresses practical challenges like missing modalities. The scope note clarifies that this leaf specifically handles partial observability with identifiability proofs, excluding full observability settings or purely empirical contrastive applications.

Among the three contributions analyzed, the identifiability result for MMCL shows the most substantial prior work overlap: among ten candidates examined, two appear refutable. The latent partial causal model itself and the disentanglement potential claim each examined ten candidates with zero refutable matches. This pattern suggests the core modeling framework may be more novel than the identifiability guarantee, though the limited search scope (thirty total candidates across all contributions) means these statistics reflect top-ranked semantic matches rather than exhaustive coverage. The identifiability contribution's overlap likely stems from existing work on contrastive learning theory or multi-view identifiability within the same theoretical branch.

Given the sparse taxonomy leaf (one sibling) and the limited literature search (thirty candidates), the work appears to occupy a relatively novel position within multimodal causal representation learning. The identifiability result shows some overlap with prior theoretical work, while the partial causal modeling framework and disentanglement claims exhibit less direct precedent among examined candidates. However, the analysis does not cover the full breadth of causal inference or contrastive learning literature, so these impressions remain provisional pending broader review.

Taxonomy

Core-task Taxonomy Papers
31
3
Claimed Contributions
30
Contribution Candidate Papers Compared
2
Refutable Paper

Research Landscape Overview

Core task: multimodal representation learning with latent partial causal models. This field addresses the challenge of learning meaningful representations from multiple data modalities when only a subset of underlying causal variables is observed. The taxonomy reveals a landscape organized around six main branches. Theoretical Foundations and Identifiability explores when and how latent causal structures can be uniquely recovered from partial observations, with works like Multi-View Partial Observability[7] and Beyond DAGs Latent[0] examining identifiability guarantees under various structural assumptions. Causal Inference and Effect Estimation focuses on estimating treatment effects and causal relationships in multimodal settings, while Debiasing and Robustness tackles confounding and spurious correlations that arise when modalities are misaligned or incomplete. Medical and Healthcare Applications, Domain-Specific Predictive Applications, and Physical and Perceptual Grounding represent applied branches where these theoretical insights are deployed—ranging from clinical decision support and supply chain forecasting to audio-visual scene understanding. Several active research directions emerge across these branches. One central tension involves balancing identifiability guarantees with practical flexibility: some studies impose strong structural constraints to ensure theoretical recovery of latent causes, while others prioritize robustness to real-world violations like missing modalities or distribution shift. Another theme concerns the interplay between representation learning and causal discovery—whether to first learn disentangled features or jointly infer causal graphs. Beyond DAGs Latent[0] sits squarely within the Theoretical Foundations branch, specifically addressing identifiability under partial observability. It shares conceptual ground with Multi-View Partial Observability[7], which also examines how multiple views can compensate for unobserved confounders, though the two may differ in their structural assumptions or the classes of models they consider identifiable. This positioning highlights the work's emphasis on foundational guarantees rather than immediate application, contrasting with more task-driven efforts in healthcare or predictive domains.

Claimed Contributions

Latent Partial Causal Model for Multimodal Data

The authors introduce a new generative model that moves beyond traditional DAG assumptions by using latent coupled variables connected via undirected edges to represent knowledge transfer across modalities. This model is specifically designed to capture the heterogeneous generative processes underlying large-scale multimodal datasets.

10 retrieved papers
Identifiability Result for Multimodal Contrastive Learning

Under specific statistical assumptions, the authors prove that MMCL recovers the true latent variables up to simple transformations (linear on hyperspheres, permutation on convex bodies). This provides a theoretical explanation for why MMCL works and connects it to the proposed generative model.

10 retrieved papers
Can Refute
Disentanglement Potential of MMCL

The theoretical results reveal that MMCL has component-wise disentanglement capabilities, which the authors claim is the first such guarantee for MMCL. This finding enables practical applications such as few-shot learning and domain generalization using pre-trained models like CLIP.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Latent Partial Causal Model for Multimodal Data

The authors introduce a new generative model that moves beyond traditional DAG assumptions by using latent coupled variables connected via undirected edges to represent knowledge transfer across modalities. This model is specifically designed to capture the heterogeneous generative processes underlying large-scale multimodal datasets.

Contribution

Identifiability Result for Multimodal Contrastive Learning

Under specific statistical assumptions, the authors prove that MMCL recovers the true latent variables up to simple transformations (linear on hyperspheres, permutation on convex bodies). This provides a theoretical explanation for why MMCL works and connects it to the proposed generative model.

Contribution

Disentanglement Potential of MMCL

The theoretical results reveal that MMCL has component-wise disentanglement capabilities, which the authors claim is the first such guarantee for MMCL. This finding enables practical applications such as few-shot learning and domain generalization using pre-trained models like CLIP.

Table of Contents

Beyond DAGs: A Latent Partial Causal Model for Multimodal Learning | Novelty Validation