MetaCaDI: A Meta-Learning Framework for Scalable Causal Discovery with Unknown Interventions

• URL: http://arxiv.org/abs/2510.22298v1
• Date: Sat, 25 Oct 2025 13:59:42 GMT
• Title: MetaCaDI: A Meta-Learning Framework for Scalable Causal Discovery with Unknown Interventions
• Authors: Hans Jarett Ong, Yoichi Chikahara, Tomoharu Iwata,
• Abstract summary: We introduce MetaCaDI, the first framework to cast the joint discovery of a causal graph and unknown interventions as a meta-learning problem.<n>A key innovation is our model's analytical adaptation, which uses a closed-form solution to bypass expensive and potentially unstable gradient-based bilevel optimization.<n>It excels at both causal graph recovery and identifying intervention targets from as few as 10 data instances, proving its robustness in data-scarce scenarios.
• Score: 18.13509245960298
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Uncovering the underlying causal mechanisms of complex real-world systems remains a significant challenge, as these systems often entail high data collection costs and involve unknown interventions. We introduce MetaCaDI, the first framework to cast the joint discovery of a causal graph and unknown interventions as a meta-learning problem. MetaCaDI is a Bayesian framework that learns a shared causal graph structure across multiple experiments and is optimized to rapidly adapt to new, few-shot intervention target prediction tasks. A key innovation is our model's analytical adaptation, which uses a closed-form solution to bypass expensive and potentially unstable gradient-based bilevel optimization. Extensive experiments on synthetic and complex gene expression data demonstrate that MetaCaDI significantly outperforms state-of-the-art methods. It excels at both causal graph recovery and identifying intervention targets from as few as 10 data instances, proving its robustness in data-scarce scenarios.

Related papers

• Can Large Language Models Help Experimental Design for Causal Discovery? [94.66802142727883]
  Large Language Model Guided Intervention Targeting (LeGIT) is a robust framework that effectively incorporates LLMs to augment existing numerical approaches for the intervention targeting in causal discovery.<n>LeGIT demonstrates significant improvements and robustness over existing methods and even surpasses humans.
  arXiv  Detail & Related papers  (2025-03-03T03:43:05Z)
• DAG-aware Transformer for Causal Effect Estimation [0.8192907805418583]
  Causal inference is a critical task across fields such as healthcare, economics, and the social sciences.<n>In this paper, we present a novel transformer-based method for causal inference that overcomes these challenges.<n>The core innovation of our model lies in its integration of causal Directed Acyclic Graphs (DAGs) directly into the attention mechanism.
  arXiv  Detail & Related papers  (2024-10-13T23:17:58Z)
• 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)
• Large-Scale Targeted Cause Discovery via Learning from Simulated Data [66.51307552703685]
  We propose a novel machine learning approach for inferring causal variables of a target variable from observations.<n>We train a neural network using supervised learning on simulated data to infer causality.<n> Empirical results demonstrate superior performance in identifying causal relationships within large-scale gene regulatory networks.
  arXiv  Detail & Related papers  (2024-08-29T02:21:11Z)
• Multi-Agent Causal Discovery Using Large Language Models [10.020595983728482]
  Causal discovery is a critical research area in machine learning.<n>We introduce the Multi-Agent Causal Discovery Framework (MAC)<n>It consists of two key modules: the Debate-Coding Module (DCM) and the Meta-Debate Module (MDM)
  arXiv  Detail & Related papers  (2024-07-21T06:21:47Z)
• 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)
• Multi-scale Fusion Fault Diagnosis Method Based on Two-Dimensionaliztion Sequence in Complex Scenarios [0.0]
  Rolling bearings are critical components in rotating machinery, and their faults can cause severe damage. Early detection of abnormalities is crucial to prevent catastrophic accidents. Traditional and intelligent methods have been used to analyze time series data, but in real-life scenarios, sensor data is often noisy and cannot be accurately characterized in the time domain. This paper proposes an improved convolutional neural network method with a multi-scale feature fusion model and deep learning compression techniques for deployment in industrial scenarios.
  arXiv  Detail & Related papers  (2023-04-11T13:05:50Z)
• 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)
• Meta Adversarial Perturbations [66.43754467275967]
  We show the existence of a meta adversarial perturbation (MAP) MAP causes natural images to be misclassified with high probability after being updated through only a one-step gradient ascent update. We show that these perturbations are not only image-agnostic, but also model-agnostic, as a single perturbation generalizes well across unseen data points and different neural network architectures.
  arXiv  Detail & Related papers  (2021-11-19T16:01:45Z)
• 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)

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.