Partial Effective Information Decomposition for Synergistic Causality
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, ...
| Authors | Mingzhe Yang, Shuo Wang, Jiang Zhang |
|---|---|
| Primary category | stat.ML |
| Published | 5 May 2026 |
| Last updated at source | 5 May 2026 |
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