CausalSteward: An Agentic Divide-Conquer-Combine Copilot for Causal Discovery

• URL: http://arxiv.org/abs/2607.01936v1
• Date: Thu, 02 Jul 2026 09:29:10 GMT
• Title: CausalSteward: An Agentic Divide-Conquer-Combine Copilot for Causal Discovery
• Abstract summary: We introduce CausalSTeward (CAST), a novel human-in-the-loop framework for interactively assembling large causal models.<n>CaulSteward is a multi-agent collaborative system that tackles high-dimensional causality through a divide-and-conquer approach.
• Score: 30.76801786098245
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Learning causal models from high-dimensional data is a significant challenge, particularly in real-world settings where violations of core assumptions lead to causal identifiability issues. Although massive amounts of prior knowledge are available, and contain valuable causal information, effectively integrating this knowledge into the causal discovery process remains an open problem. We introduce CausalSTeward (CAST), a novel human-in-the-loop framework for interactively assembling large causal models. CausalSteward is a multi-agent collaborative system that tackles high-dimensional causality through a divide-and-conquer approach where large clusters of variables are iteratively partitioned and then separately analyzed. Our framework fuses prior knowledge with a data-driven approach by using tailored tools such as retrieval augmented generation and conditional independence tests. Finally, we use this work to examine the capabilities and limitations of causal reasoning in multi-agent frameworks, and how the human-in-the-loop can contribute to accurate and trustworthy results.

Related papers

• Causal Discovery with Inverted Self-attention for Multivariate Time Series [21.018583540683917]
  We propose a novel framework that leverages self-attention mechanisms within the transformer architecture for causal discovery.<n>Our approach emphasizes latent and indirect causal relationships by inverting tokens and inducing sparsity in attention scores.<n>We also develop a global causal algorithm to identify global causal links, providing a holistic metric for causal influence.
  arXiv  Detail & Related papers  (2026-07-30T13:49:46Z)
• Causal Ensemble Agent: Hierarchical Causal Discovery with LLM-guided Expert Reweighting [95.24017293537025]
  Causal Ensemble Agent (CEA) is a novel framework that aggregates structural insights from statistical discovery experts across different graph levels.<n>CEA achieves the strongest overall performance across a wide range of causal discovery methods.
  arXiv  Detail & Related papers  (2026-06-09T09:09:07Z)
• Use What You Know: Causal Foundation Models with Partial Graphs [97.91863420927866]
  Recently proposed Causal Foundation Models (CFMs) promise a more unified approach by amortising causal discovery and inference in a single step.<n>We bridge this gap by introducing methods to condition CFMs on causal information, such as the causal graph or more readily available ancestral information.
  arXiv  Detail & Related papers  (2026-02-16T17:56:37Z)
• Relational Causal Discovery with Latent Confounders [11.833422037033001]
  We propose RelFCI, a complete causal discovery algorithm for relational data with latent confounders.<n>We present results demonstrating the effectiveness of RelFCI in identifying the correct causal structure in relational causal models with latent confounders.
  arXiv  Detail & Related papers  (2025-07-02T13:29:35Z)
• 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)
• Exploring Multi-Modal Data with Tool-Augmented LLM Agents for Precise Causal Discovery [45.777770849667775]
  We introduce MATMCD, a multi-agent system powered by tool-augmented LLMs.<n>Our empirical study suggests the significant potential of multi-modality enhanced causal discovery.
  arXiv  Detail & Related papers  (2024-12-18T09:50:00Z)
• 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)
• Weakly-supervised causal discovery based on fuzzy knowledge and complex data complementarity [4.637772575470497]
  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.
  arXiv  Detail & Related papers  (2024-05-14T15:39:22Z)
• Discovering and Reasoning of Causality in the Hidden World with Large Language Models [109.62442253177376]
  We develop a new framework termed Causal representatiOn AssistanT (COAT) to propose useful measured variables for causal discovery.<n>Instead of directly inferring causality with Large language models (LLMs), COAT constructs feedback from intermediate causal discovery results to LLMs to refine the proposed variables.
  arXiv  Detail & Related papers  (2024-02-06T12:18:54Z)
• 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)
• BayesIMP: Uncertainty Quantification for Causal Data Fusion [52.184885680729224]
  We study the causal data fusion problem, where datasets pertaining to multiple causal graphs are combined to estimate the average treatment effect of a target variable. We introduce a framework which combines ideas from probabilistic integration and kernel mean embeddings to represent interventional distributions in the reproducing kernel Hilbert space.
  arXiv  Detail & Related papers  (2021-06-07T10:14:18Z)
• Disentangling Observed Causal Effects from Latent Confounders using Method of Moments [67.27068846108047]
  We provide guarantees on identifiability and learnability under mild assumptions. We develop efficient algorithms based on coupled tensor decomposition with linear constraints to obtain scalable and guaranteed solutions.
  arXiv  Detail & Related papers  (2021-01-17T07:48:45Z)

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.