Partial Effective Information Decomposition for Synergistic Causality

• URL: http://arxiv.org/abs/2605.03267v1
• Date: Tue, 05 May 2026 01:39:17 GMT
• Title: Partial Effective Information Decomposition for Synergistic Causality
• Abstract summary: This paper proposes a framework that decomposes the influence of multiple source variables on a target variable under maximum-entropy interventions.<n>Based on this framework, it is possible to define causal graphs containing hyperedges as well as downward causation.<n>Applying the framework to a machine-learning-based air quality forecasting task on KnowAir-V2, we demonstrate that PEID can extract interpretable inter-station causal structures.
• Score: 7.157956561479179
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Causality is a central topic in scientific inquiry, yet for complex systems, the identification and analysis of synergistic causation remain a challenging and fundamental problem. In the context of causal relations among multivariate variables, a decomposition framework grounded in interventionist causation is still lacking. To address this gap, this paper proposes Partial Effective Information Decomposition (PEID), a framework that decomposes the influence of multiple source variables on a target variable under maximum-entropy interventions into unique and synergistic information, thereby providing a unified and computable characterization of synergistic causal relations. Theoretically, in the three-variable case, the proposed framework is compatible with the major axioms of Partial Information Decomposition (PID). Empirically, under maximum-entropy interventions, correlations among input variables are removed, causing redundancy to vanish and thereby enabling PEID to compute synergistic relations. Furthermore, based on this framework, it is possible to define causal graphs containing hyperedges as well as downward causation, thus offering a unified toolkit for analyzing cross-scale and multivariate causal mechanisms in complex systems. Finally, applying the framework to a machine-learning-based air quality forecasting task on KnowAir-V2, we demonstrate that PEID can extract interpretable inter-station causal structures from a learned dynamical model. These results suggest that PEID provides a general interventionist information-theoretic tool for analyzing multivariate and synergistic causal mechanisms in complex systems.

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)
• Decomposing Interventional Causality into Synergistic, Redundant, and Unique Components [0.0]
  We introduce a novel framework for decomposing interventional causal effects into synergistic, redundant, and unique components.<n>We develop a mathematical approach that systematically quantifies how causal power is distributed among variables in a system.
  arXiv  Detail & Related papers  (2025-01-20T12:34:51Z)
• Higher order definition of causality by optimally conditioned transfer entropy [0.0]
  We propose a generalization and refinement of the information-theoretic approach to causal inference.<n>In particular, we ascribe the causal role to the multivariate causal set but emphnot to individual inputs.<n>We demonstrate this concept by application to illustrative theoretical examples as well as a biophysically realistic simulation of biological neuronal dynamics.
  arXiv  Detail & Related papers  (2024-08-30T12:04:51Z)
• 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)
• iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models [48.33685559041322]
  This paper focuses on identifying the causal mechanism shifts in two or more related datasets over the same set of variables. Code implementing the proposed method is open-source and publicly available at https://github.com/kevinsbello/iSCAN.
  arXiv  Detail & Related papers  (2023-06-30T01:48:11Z)
• A Causal Framework for Decomposing Spurious Variations [68.12191782657437]
  We develop tools for decomposing spurious variations in Markovian and Semi-Markovian models. We prove the first results that allow a non-parametric decomposition of spurious effects. The described approach has several applications, ranging from explainable and fair AI to questions in epidemiology and medicine.
  arXiv  Detail & Related papers  (2023-06-08T09:40:28Z)
• Quantify the Causes of Causal Emergence: Critical Conditions of Uncertainty and Asymmetry in Causal Structure [0.5372002358734439]
  Investigation of causal relationships based on statistical and informational theories have posed an interesting and valuable challenge to large-scale models. This paper introduces a framework for assessing numerical conditions of Causal Emergence as theoretical constraints of its occurrence.
  arXiv  Detail & Related papers  (2022-12-03T06:35:54Z)
• Causality-Based Multivariate Time Series Anomaly Detection [63.799474860969156]
  We formulate the anomaly detection problem from a causal perspective and view anomalies as instances that do not follow the regular causal mechanism to generate the multivariate data. We then propose a causality-based anomaly detection approach, which first learns the causal structure from data and then infers whether an instance is an anomaly relative to the local causal mechanism. We evaluate our approach with both simulated and public datasets as well as a case study on real-world AIOps applications.
  arXiv  Detail & Related papers  (2022-06-30T06:00:13Z)
• Variational Causal Networks: Approximate Bayesian Inference over Causal Structures [132.74509389517203]
  We introduce a parametric variational family modelled by an autoregressive distribution over the space of discrete DAGs. In experiments, we demonstrate that the proposed variational posterior is able to provide a good approximation of the true posterior.
  arXiv  Detail & Related papers  (2021-06-14T17:52:49Z)
• A Critical View of the Structural Causal Model [89.43277111586258]
  We show that one can identify the cause and the effect without considering their interaction at all. We propose a new adversarial training method that mimics the disentangled structure of the causal model. Our multidimensional method outperforms the literature methods on both synthetic and real world datasets.
  arXiv  Detail & Related papers  (2020-02-23T22:52:28Z)

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.