Weakly-supervised causal discovery based on fuzzy knowledge and complex data complementarity

• URL: http://arxiv.org/abs/2405.08699v1
• Date: Tue, 14 May 2024 15:39:22 GMT
• Title: Weakly-supervised causal discovery based on fuzzy knowledge and complex data complementarity
• Authors: Wenrui Li, Wei Zhang, Qinghao Zhang, Xuegong Zhang, Xiaowo Wang,
• Abstract summary: We propose a novel weakly-supervised fuzzy knowledge and data co-driven causal discovery method named KEEL. KEEL adopts a fuzzy causal knowledge schema to encapsulate diverse types of fuzzy knowledge, and forms corresponding weakened constraints. It can enhance the generalization and robustness of causal discovery, especially in high-dimensional and small-sample scenarios.
• Score: 4.637772575470497
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Causal discovery based on observational data is important for deciphering the causal mechanism behind complex systems. However, the effectiveness of existing causal discovery methods is limited due to inferior prior knowledge, domain inconsistencies, and the challenges of high-dimensional datasets with small sample sizes. To address this gap, we propose a novel weakly-supervised fuzzy knowledge and data co-driven causal discovery method named KEEL. KEEL adopts a fuzzy causal knowledge schema to encapsulate diverse types of fuzzy knowledge, and forms corresponding weakened constraints. This schema not only lessens the dependency on expertise but also allows various types of limited and error-prone fuzzy knowledge to guide causal discovery. It can enhance the generalization and robustness of causal discovery, especially in high-dimensional and small-sample scenarios. In addition, we integrate the extended linear causal model (ELCM) into KEEL for dealing with the multi-distribution and incomplete data. Extensive experiments with different datasets demonstrate the superiority of KEEL over several state-of-the-art methods in accuracy, robustness and computational efficiency. For causal discovery in real protein signal transduction processes, KEEL outperforms the benchmark method with limited data. In summary, KEEL is effective to tackle the causal discovery tasks with higher accuracy while alleviating the requirement for extensive domain expertise.

Related papers

• CAnDOIT: Causal Discovery with Observational and Interventional Data from Time-Series [4.008958683836471]
  CAnDOIT is a causal discovery method to reconstruct causal models using both observational and interventional data. The use of interventional data in the causal analysis is crucial for real-world applications, such as robotics. A Python implementation of CAnDOIT has also been developed and is publicly available on GitHub.
  arXiv  Detail & Related papers  (2024-10-03T13:57:08Z)
• Targeted Cause Discovery with Data-Driven Learning [66.86881771339145]
  We propose a novel machine learning approach for inferring causal variables of a target variable from observations. We employ a neural network trained to identify causality through supervised learning on simulated data. Empirical results demonstrate the effectiveness of our method in identifying causal relationships within large-scale gene regulatory networks.
  arXiv  Detail & Related papers  (2024-08-29T02:21:11Z)
• Multi-modal Causal Structure Learning and Root Cause Analysis [67.67578590390907]
  We propose Mulan, a unified multi-modal causal structure learning method for root cause localization. We leverage a log-tailored language model to facilitate log representation learning, converting log sequences into time-series data. We also introduce a novel key performance indicator-aware attention mechanism for assessing modality reliability and co-learning a final causal graph.
  arXiv  Detail & Related papers  (2024-02-04T05:50:38Z)
• Causal disentanglement of multimodal data [1.589226862328831]
  We introduce a causal representation learning algorithm (causalPIMA) that can use multimodal data and known physics to discover important features with causal relationships. Our results demonstrate the capability of learning an interpretable causal structure while simultaneously discovering key features in a fully unsupervised setting.
  arXiv  Detail & Related papers  (2023-10-27T20:30:11Z)
• CUTS+: High-dimensional Causal Discovery from Irregular Time-series [13.84185941100574]
  We propose CUTS+, which is built on the Granger-causality-based causal discovery method CUTS. We show that CUTS+ largely improves the causal discovery performance on high-dimensional data with different types of irregular sampling.
  arXiv  Detail & Related papers  (2023-05-10T04:20:36Z)
• Evaluation of Induced Expert Knowledge in Causal Structure Learning by NOTEARS [1.5469452301122175]
  We study the impact of expert knowledge on causal relations in the form of additional constraints used in the formulation of the nonparametric NOTEARS model. We found that (i) knowledge that corrects the mistakes of the NOTEARS model can lead to statistically significant improvements, (ii) constraints on active edges have a larger positive impact on causal discovery than inactive edges, and surprisingly, (iii) the induced knowledge does not correct on average more incorrect active and/or inactive edges than expected.
  arXiv  Detail & Related papers  (2023-01-04T20:39:39Z)
• Trust Your $\nabla$: Gradient-based Intervention Targeting for Causal Discovery [49.084423861263524]
  In this work, we propose a novel Gradient-based Intervention Targeting method, abbreviated GIT. GIT 'trusts' the gradient estimator of a gradient-based causal discovery framework to provide signals for the intervention acquisition function. We provide extensive experiments in simulated and real-world datasets and demonstrate that GIT performs on par with competitive baselines.
  arXiv  Detail & Related papers  (2022-11-24T17:04:45Z)
• BaCaDI: Bayesian Causal Discovery with Unknown Interventions [118.93754590721173]
  BaCaDI operates in the continuous space of latent probabilistic representations of both causal structures and interventions. In experiments on synthetic causal discovery tasks and simulated gene-expression data, BaCaDI outperforms related methods in identifying causal structures and intervention targets.
  arXiv  Detail & Related papers  (2022-06-03T16:25:48Z)
• Principled Knowledge Extrapolation with GANs [92.62635018136476]
  We study counterfactual synthesis from a new perspective of knowledge extrapolation. We show that an adversarial game with a closed-form discriminator can be used to address the knowledge extrapolation problem. Our method enjoys both elegant theoretical guarantees and superior performance in many scenarios.
  arXiv  Detail & Related papers  (2022-05-21T08:39:42Z)
• Learning Neural Causal Models with Active Interventions [83.44636110899742]
  We introduce an active intervention-targeting mechanism which enables a quick identification of the underlying causal structure of the data-generating process. Our method significantly reduces the required number of interactions compared with random intervention targeting. We demonstrate superior performance on multiple benchmarks from simulated to real-world data.
  arXiv  Detail & Related papers  (2021-09-06T13:10:37Z)
• Causal Discovery from Incomplete Data: A Deep Learning Approach [21.289342482087267]
  Imputated Causal Learning is proposed to perform iterative missing data imputation and causal structure discovery. We show that ICL can outperform state-of-the-art methods under different missing data mechanisms.
  arXiv  Detail & Related papers  (2020-01-15T14:28:21Z)

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.