Temporal Latent Variable Structural Causal Model for Causal Discovery under External Interferences

• URL: http://arxiv.org/abs/2511.10031v1
• Date: Fri, 14 Nov 2025 01:27:14 GMT
• Title: Temporal Latent Variable Structural Causal Model for Causal Discovery under External Interferences
• Authors: Ruichu Cai, Xiaokai Huang, Wei Chen, Zijian Li, Zhifeng Hao,
• Abstract summary: We introduce latent variables to represent unobserved factors that affect the observed data.<n>Specifically, to capture the causal strength and adjacency information, we propose a new temporal latent variable structural causal model.<n>Considering that expert knowledge can provide information about unknown interferences in certain scenarios, we develop a method that facilitates the incorporation of prior knowledge into parameter learning.
• Score: 53.308122815325326
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Inferring causal relationships from observed data is an important task, yet it becomes challenging when the data is subject to various external interferences. Most of these interferences are the additional effects of external factors on observed variables. Since these external factors are often unknown, we introduce latent variables to represent these unobserved factors that affect the observed data. Specifically, to capture the causal strength and adjacency information, we propose a new temporal latent variable structural causal model, incorporating causal strength and adjacency coefficients that represent the causal relationships between variables. Considering that expert knowledge can provide information about unknown interferences in certain scenarios, we develop a method that facilitates the incorporation of prior knowledge into parameter learning based on Variational Inference, to guide the model estimation. Experimental results demonstrate the stability and accuracy of our proposed method.

Related papers

• Unsupervised Pairwise Causal Discovery on Heterogeneous Data using Mutual Information Measures [49.1574468325115]
  Causal Discovery is a technique that tackles the challenge by analyzing the statistical properties of the constituent variables. We question the current (possibly misleading) baseline results on the basis that they were obtained through supervised learning. In consequence, we approach this problem in an unsupervised way, using robust Mutual Information measures.
  arXiv  Detail & Related papers  (2024-08-01T09:11:08Z)
• Causal Inference with Latent Variables: Recent Advances and Future Prospectives [43.04559575298597]
  Causal inference (CI) aims to infer intrinsic causal relations among variables of interest. The lack of observation of important variables severely compromises the reliability of CI methods. Various consequences can be incurred if these latent variables are carelessly handled.
  arXiv  Detail & Related papers  (2024-06-20T03:15:53Z)
• On the Identification of Temporally Causal Representation with Instantaneous Dependence [50.14432597910128]
  Temporally causal representation learning aims to identify the latent causal process from time series observations. Most methods require the assumption that the latent causal processes do not have instantaneous relations. We propose an textbfIDentification framework for instantanetextbfOus textbfLatent dynamics.
  arXiv  Detail & Related papers  (2024-05-24T08:08:05Z)
• Identifiable Latent Polynomial Causal Models Through the Lens of Change [82.14087963690561]
  Causal representation learning aims to unveil latent high-level causal representations from observed low-level data.<n>One of its primary tasks is to provide reliable assurance of identifying these latent causal models, known as identifiability.
  arXiv  Detail & Related papers  (2023-10-24T07:46:10Z)
• Nonlinearity, Feedback and Uniform Consistency in Causal Structural Learning [0.8158530638728501]
  Causal Discovery aims to find automated search methods for learning causal structures from observational data. This thesis focuses on two questions in causal discovery: (i) providing an alternative definition of k-Triangle Faithfulness that (i) is weaker than strong faithfulness when applied to the Gaussian family of distributions, and (ii) under the assumption that the modified version of Strong Faithfulness holds.
  arXiv  Detail & Related papers  (2023-08-15T01:23:42Z)
• Towards Causal Representation Learning and Deconfounding from Indefinite Data [17.793702165499298]
  Non-statistical data (e.g., images, text, etc.) encounters significant conflicts in terms of properties and methods with traditional causal data. We redefine causal data from two novel perspectives and then propose three data paradigms. We implement the above designs as a dynamic variational inference model, tailored to learn causal representation from indefinite data.
  arXiv  Detail & Related papers  (2023-05-04T08:20:37Z)
• Identifying Weight-Variant Latent Causal Models [82.14087963690561]
  We find that transitivity acts as a key role in impeding the identifiability of latent causal representations. Under some mild assumptions, we can show that the latent causal representations can be identified up to trivial permutation and scaling. We propose a novel method, termed Structural caUsAl Variational autoEncoder, which directly learns latent causal representations and causal relationships among them.
  arXiv  Detail & Related papers  (2022-08-30T11:12:59Z)
• Causal Discovery in Linear Structural Causal Models with Deterministic Relations [27.06618125828978]
  We focus on the task of causal discovery form observational data. We derive a set of necessary and sufficient conditions for unique identifiability of the causal structure.
  arXiv  Detail & Related papers  (2021-10-30T21:32:42Z)
• Latent Causal Invariant Model [128.7508609492542]
  Current supervised learning can learn spurious correlation during the data-fitting process. We propose a Latent Causal Invariance Model (LaCIM) which pursues causal prediction.
  arXiv  Detail & Related papers  (2020-11-04T10:00:27Z)
• Estimating Causal Effects with the Neural Autoregressive Density Estimator [6.59529078336196]
  We use neural autoregressive density estimators to estimate causal effects within the Pearl's do-calculus framework. We show that the approach can retrieve causal effects from non-linear systems without explicitly modeling the interactions between the variables.
  arXiv  Detail & Related papers  (2020-08-17T13:12:38Z)

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.