Long-form instructional videos require automatic chaptering for efficient organization and navigation. While long-context language models can predict chapters from textualized video representations, they become costly for content-dense videos with long transcripts, smooth topic transitions, and detailed chapter descriptions. A scalable segment-then-caption paradigm reduces this cost but suffers from boundary error propagation and fragmented cross-chapter context. We propose CausalChapter, a causal-inspired interventional dependency enhancement framework for long-video chaptering. Our method estimates the observable influence of semantic units and context segments through lightweight masking and removal interventions. For boundary localization, it detects dependency changes between adjacent temporal windows via masking-based feature reconstruction; for chapter description generation, it selects cross-chapter contexts according to their influence on generated descriptions. Experiments on long-video chaptering benchmarks show consistent improvements in boundary localization, description quality, and cross-chapter coherence.