Toward Temporal Causal Representation Learning with Tensor Decomposition

• URL: http://arxiv.org/abs/2507.14126v1
• Date: Fri, 18 Jul 2025 17:55:42 GMT
• Title: Toward Temporal Causal Representation Learning with Tensor Decomposition
• Authors: Jianhong Chen, Meng Zhao, Mostafa Reisi Gahrooei, Xubo Yue,
• Abstract summary: In this paper, we focus on modeling causal representation learning based on the transformed information.<n>We propose CaRTeD, a joint learning framework that integrates temporal causal representation learning with irregular tensor decomposition.<n>Our results fill the gap in theoretical guarantees for the convergence of state-of-the-art irregular tensor decomposition.
• Score: 5.288554155235167
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Temporal causal representation learning is a powerful tool for uncovering complex patterns in observational studies, which are often represented as low-dimensional time series. However, in many real-world applications, data are high-dimensional with varying input lengths and naturally take the form of irregular tensors. To analyze such data, irregular tensor decomposition is critical for extracting meaningful clusters that capture essential information. In this paper, we focus on modeling causal representation learning based on the transformed information. First, we present a novel causal formulation for a set of latent clusters. We then propose CaRTeD, a joint learning framework that integrates temporal causal representation learning with irregular tensor decomposition. Notably, our framework provides a blueprint for downstream tasks using the learned tensor factors, such as modeling latent structures and extracting causal information, and offers a more flexible regularization design to enhance tensor decomposition. Theoretically, we show that our algorithm converges to a stationary point. More importantly, our results fill the gap in theoretical guarantees for the convergence of state-of-the-art irregular tensor decomposition. Experimental results on synthetic and real-world electronic health record (EHR) datasets (MIMIC-III), with extensive benchmarks from both phenotyping and network recovery perspectives, demonstrate that our proposed method outperforms state-of-the-art techniques and enhances the explainability of causal representations.

Related papers

• Score-Based Model for Low-Rank Tensor Recovery [49.158601255093416]
  Low-rank tensor decompositions (TDs) provide an effective framework for multiway data analysis.<n>Traditional TD methods rely on predefined structural assumptions, such as CP or Tucker decompositions.<n>We propose a score-based model that eliminates the need for predefined structural or distributional assumptions.
  arXiv  Detail & Related papers  (2025-06-27T15:05:37Z)
• Low-Rank Implicit Neural Representation via Schatten-p Quasi-Norm and Jacobian Regularization [49.158601255093416]
  We propose a CP-based low-rank tensor function parameterized by neural networks for implicit neural representation.<n>For smoothness, we propose a regularization term based on the spectral norm of the Jacobian and Hutchinson's trace estimator.<n>Our proposed smoothness regularization is SVD-free and avoids explicit chain rule derivations.
  arXiv  Detail & Related papers  (2025-06-27T11:23:10Z)
• Diffusion Model with Cross Attention as an Inductive Bias for Disentanglement [58.9768112704998]
  Disentangled representation learning strives to extract the intrinsic factors within observed data. We introduce a new perspective and framework, demonstrating that diffusion models with cross-attention can serve as a powerful inductive bias. This is the first work to reveal the potent disentanglement capability of diffusion models with cross-attention, requiring no complex designs.
  arXiv  Detail & Related papers  (2024-02-15T05:07:54Z)
• Specify Robust Causal Representation from Mixed Observations [35.387451486213344]
  Learning representations purely from observations concerns the problem of learning a low-dimensional, compact representation which is beneficial to prediction models. We develop a learning method to learn such representation from observational data by regularizing the learning procedure with mutual information measures. We theoretically and empirically show that the models trained with the learned causal representations are more robust under adversarial attacks and distribution shifts.
  arXiv  Detail & Related papers  (2023-10-21T02:18:35Z)
• Provable Tensor Completion with Graph Information [49.08648842312456]
  We introduce a novel model, theory, and algorithm for solving the dynamic graph regularized tensor completion problem. We develop a comprehensive model simultaneously capturing the low-rank and similarity structure of the tensor. In terms of theory, we showcase the alignment between the proposed graph smoothness regularization and a weighted tensor nuclear norm.
  arXiv  Detail & Related papers  (2023-10-04T02:55:10Z)
• SWoTTeD: An Extension of Tensor Decomposition to Temporal Phenotyping [0.0]
  We propose SWoTTeD (Sliding Window for Temporal Decomposition), a novel method to discover hidden temporal patterns. We validate our proposal using both synthetic and real-world datasets, and we present an original usecase using data from the Greater Paris University Hospital. The results show that SWoTTeD achieves at least as accurate reconstruction as recent state-of-the-art tensor decomposition models.
  arXiv  Detail & Related papers  (2023-10-02T13:42:11Z)
• ER: Equivariance Regularizer for Knowledge Graph Completion [107.51609402963072]
  We propose a new regularizer, namely, Equivariance Regularizer (ER) ER can enhance the generalization ability of the model by employing the semantic equivariance between the head and tail entities. The experimental results indicate a clear and substantial improvement over the state-of-the-art relation prediction methods.
  arXiv  Detail & Related papers  (2022-06-24T08:18:05Z)
• Amortized Inference for Causal Structure Learning [72.84105256353801]
  Learning causal structure poses a search problem that typically involves evaluating structures using a score or independence test. We train a variational inference model to predict the causal structure from observational/interventional data. Our models exhibit robust generalization capabilities under substantial distribution shift.
  arXiv  Detail & Related papers  (2022-05-25T17:37:08Z)
• Generalizable Information Theoretic Causal Representation [37.54158138447033]
  We propose to learn causal representation from observational data by regularizing the learning procedure with mutual information measures according to our hypothetical causal graph. The optimization involves a counterfactual loss, based on which we deduce a theoretical guarantee that the causality-inspired learning is with reduced sample complexity and better generalization ability.
  arXiv  Detail & Related papers  (2022-02-17T00:38:35Z)
• Low-Rank and Sparse Enhanced Tucker Decomposition for Tensor Completion [3.498620439731324]
  We introduce a unified low-rank and sparse enhanced Tucker decomposition model for tensor completion. Our model possesses a sparse regularization term to promote a sparse core tensor, which is beneficial for tensor data compression. It is remarkable that our model is able to deal with different types of real-world data sets, since it exploits the potential periodicity and inherent correlation properties appeared in tensors.
  arXiv  Detail & Related papers  (2020-10-01T12:45:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.