<p>Classroom behaviour recognition is easily disrupted by visual confounders such as dense crowds, occlusions, viewpoint and illumination changes, and closely co-occurring actions. These factors hinder the learning of robust and interpretable behaviour representations. Causal inference has been introduced into this task, but existing methods typically treat spatio-temporal confounders as a single factor and ignore the distinct roles of spatial and temporal information. This contrasts with evidence from cognitive science that “where” and “when” information is processed by partly separate neural systems. Existing classroom behaviour datasets further focus on isolated actions and rarely capture the dynamics of interaction. To address these issues, we propose CausalCIBR, a causal-inference-based framework for classroom interactive behaviour recognition, together with a new dataset, BNU-SVIBD. CausalCIBR models spatial and temporal behaviour associations as decoupled confounders, whose occurrence probabilities are calibrated using behaviour co-occurrence statistics from educational practice. Within a structural causal model, we intervene on these confounder dictionaries and use a multi-scale gated feedforward module to couple intervened features with local and global spatio-temporal context, strengthening causal signals while suppressing spurious co-occurrences. BNU-SVIBD provides annotated interactive sequences that describe the dynamic evolution of classroom interaction events, enabling fine-grained behaviour analysis. Experiments on BNU-SVIBD and other challenging benchmarks show that CausalCIBR achieves state-of-the-art performance and improved robustness under distribution shifts and confounder perturbations, effectively capturing the complexity of classroom interactions.</p>