Traditional statistical approaches primarily model associations between variables, however many scientific and practical questions require causal methods instead. These methods typically rely on assumptions about an underlying structure and are often represented by a Directed Acyclic Graph (DAG). While causal structures can be learned for single-level data, hierarchical or multi-level settings, where units (e.g., plants, students or patients) are nested within groups (e.g., environments, schools or hospitals), lack suitable methods. This article addresses such settings. These multi-level structures frequently arise in fields such as agriculture, where plants grow within different environments. Building on nonlinear structural causal models, or additive noise models, we propose an approach that facilitates causal structure learning for hierarchical causal models with additive unobserved group-level effects and group-specific causal functions. We also propose a simulation-based manner to compute hard interventions for the estimated causal model. In a simulation study, we show that the proposed method is able to identify the underlying hierarchical causal mechanism. A winter-wheat example is used to showcase how the proposed method can be used in practice and how to interpret its results.

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