Understanding the relationship between brain activity and behavior is a central goal of neuroscience. Despite significant advances, a fundamental dichotomy persists: neural activity manifests as both discrete spikes of individual neurons and collective waves of populations. Both neural codes correlate with behavior, yet correlation alone cannot determine whether waves exert a causal influence or merely reflect spiking dynamics without causal efficacy. According to the Causal Hierarchy Theorem, no amount of observational data--however extensive--can settle this question; causal conclusions require explicit structural assumptions or careful experiment designs that directly correspond to the causal effect of interest. We develop a formal framework that makes this limitation precise and constructive. Formalizing epiphenomenality via the invariance of interventional distributions in Structural Causal Models (SCMs), we derive a certificate of sufficiency from Pearl's do-calculus that specifies when variables can be removed from the model without loss of causal explainability and clarifies how interventions should be interpreted under different causal structures of spike-wave duality. The purpose of this work is not to resolve the spike-wave debate, but to reformulate it. We shift the problem from asking which signal matters most to asking under what conditions any signal can be shown to matter at all. This reframing distinguishes prediction from explanation and offers neuroscience a principled route for deciding when waves belong to mechanism and when they constitute a byproduct of underlying coordination

翻译：理解大脑活动与行为之间的关系是神经科学的核心目标。尽管取得了显著进展，但一个根本的二分法依然存在：神经活动既表现为单个神经元的离散尖峰放电，也表现为群体活动的集体波动。这两种神经编码均与行为相关，但仅凭相关性无法确定波动是否施加了因果影响，抑或仅反映了缺乏因果效力的尖峰动力学。根据因果层次定理，无论观测数据多么广泛，都无法解决这一问题；因果结论需要明确的结构假设或精心设计的实验，这些假设或实验必须直接对应于感兴趣的因果效应。我们建立了一个形式化框架，使这一限制变得精确且具有建设性。通过结构因果模型中干预分布的不变性来形式化副现象性，我们从Pearl的do-演算中推导出一个充分性证明，该证明规定了何时可以从模型中移除变量而不损失因果可解释性，并阐明了在尖峰-波二象性的不同因果结构下应如何解释干预。本工作的目的并非解决尖峰-波之争，而是对其重新表述。我们将问题从‘哪种信号最重要’转向‘在何种条件下可以证明任何信号具有重要性’。这一重构区分了预测与解释，为神经科学提供了一个原则性路径，以判断波动何时属于机制的一部分，何时构成底层协调的副产品。