Traditional spatiotemporal modeling for video anomaly detection often suffers from high computational costs and relies on heuristic assumptions (e.g., feature magnitude) that can be flawed and hinder robust representation learning. To address this, we propose LN_Net, a Lightweight Network adopting a non-interventionist strategy to overcome the pitfalls of imposing such potentially misleading priors. This approach grants the model greater flexibility to learn discriminative patterns directly from observational data. LN_Net implements this strategy through two core, efficient innovations: (1) an Efficient Temporal Modeling Module (ETMM) capturing multi-faceted temporal dynamics without convolutions, and (2) an Adaptive Focusing Module (AFM) highlighting salient temporal evidence. Our non-interventionist method achieves competitive detection accuracy (97.77% SH-AUC, 86.21% UCF-AUC). Simultaneously, it demonstrates state-of-the-art efficiency, requiring only about 1/35th the parameters, 1/135th the model size, and 1/51th the inference time compared to recent complex methods like VadCLIP. This highlights its significant practical value for deployment.