Causal Learning for Heterogeneous Subgroups Based on Nonlinear Causal Kernel Clustering

• URL: http://arxiv.org/abs/2501.11622v3
• Date: Sat, 08 Feb 2025 14:20:05 GMT
• Title: Causal Learning for Heterogeneous Subgroups Based on Nonlinear Causal Kernel Clustering
• Authors: Lu Liu, Yang Tang, Kexuan Zhang, Qiyu Sun,
• Abstract summary: The nonlinear Causal Kernel Clustering method is introduced for heterogeneous causal learning. Experimental results indicate that the method performs well in identifying heterogeneous subgroups and enhancing causal learning.
• Score: 11.9672224014053
• License:
• Abstract: Due to the challenge posed by multi-source and heterogeneous data collected from diverse environments, causal relationships among features can exhibit variations influenced by different time spans, regions, or strategies. This diversity makes a single causal model inadequate for accurately representing complex causal relationships in all observational data, a crucial consideration in causal learning. To address this challenge, the nonlinear Causal Kernel Clustering method is introduced for heterogeneous subgroup causal learning, highlighting variations in causal relationships across diverse subgroups. The main component for clustering heterogeneous subgroups lies in the construction of the $u$-centered sample mapping function with the property of unbiased estimation, which assesses the differences in potential nonlinear causal relationships in various samples and supported by causal identifiability theory. Experimental results indicate that the method performs well in identifying heterogeneous subgroups and enhancing causal learning, leading to a reduction in prediction error.

Related papers

• Identifying latent disease factors differently expressed in patient subgroups using group factor analysis [54.67330718129736]
  We propose a novel approach to uncover subgroup-specific and subgroup-common latent factors. The proposed approach, sparse Group Factor Analysis (GFA) with regularised horseshoe priors, was implemented with probabilistic programming.
  arXiv  Detail & Related papers  (2024-10-10T13:12:14Z)
• 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. 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)
• Learning Linear Causal Representations from Interventions under General Nonlinear Mixing [52.66151568785088]
  We prove strong identifiability results given unknown single-node interventions without access to the intervention targets. This is the first instance of causal identifiability from non-paired interventions for deep neural network embeddings.
  arXiv  Detail & Related papers  (2023-06-04T02:32:12Z)
• Nonparametric Identifiability of Causal Representations from Unknown Interventions [63.1354734978244]
  We study causal representation learning, the task of inferring latent causal variables and their causal relations from mixtures of the variables. Our goal is to identify both the ground truth latents and their causal graph up to a set of ambiguities which we show to be irresolvable from interventional data.
  arXiv  Detail & Related papers  (2023-06-01T10:51:58Z)
• Equivariance Allows Handling Multiple Nuisance Variables When Analyzing Pooled Neuroimaging Datasets [53.34152466646884]
  In this paper, we show how bringing recent results on equivariant representation learning instantiated on structured spaces together with simple use of classical results on causal inference provides an effective practical solution. We demonstrate how our model allows dealing with more than one nuisance variable under some assumptions and can enable analysis of pooled scientific datasets in scenarios that would otherwise entail removing a large portion of the samples.
  arXiv  Detail & Related papers  (2022-03-29T04:54:06Z)
• 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)
• A Distance Covariance-based Kernel for Nonlinear Causal Clustering in Heterogeneous Populations [1.2763567932588586]
  We introduce a distance covariance-based kernel designed specifically to measure the similarity between the underlying nonlinear causal structures of different samples. This kernel enables us to perform clustering to identify the homogeneous subpopulations. We demonstrate using our kernel for causal clustering with an application in genetics, allowing us to reason about the latent transcription factor networks regulating measured gene expression levels.
  arXiv  Detail & Related papers  (2021-06-07T10:16:34Z)
• Accounting for Unobserved Confounding in Domain Generalization [107.0464488046289]
  This paper investigates the problem of learning robust, generalizable prediction models from a combination of datasets. Part of the challenge of learning robust models lies in the influence of unobserved confounders. We demonstrate the empirical performance of our approach on healthcare data from different modalities.
  arXiv  Detail & Related papers  (2020-07-21T08:18:06Z)
• Causal network learning with non-invertible functional relationships [7.845605663563046]
  Several recent results have established the identifiability of causal DAGs with non-Gaussian and/or nonlinear structural equation models (SEMs) In this paper, we focus on nonlinear SEMs defined by non-invertible functions, which exist in many data domains. We develop a method to incorporate this test in structure learning of DAGs that contain both linear and nonlinear causal relations.
  arXiv  Detail & Related papers  (2020-04-20T21:32:05Z)
• Directionally Dependent Multi-View Clustering Using Copula Model [3.5097082077065]
  In genomics studies, there is certain directional dependence between DNA expression, DNA methylation, and RNA expression. Most of the existing multi-view clustering methods either assume an independent structure or pair-wise (non-directional) dependency. We propose a copula-based multi-view clustering model where a copula enables the model to accommodate the directional dependence existing in the datasets.
  arXiv  Detail & Related papers  (2020-03-17T02:04:10Z)

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.