Sign Language Translation (SLT) models integrate multiple visual channels, including the hands, body, and face, to generate written text, yet it remains unclear which channels the model actually relies on. We introduce Causal Stream Intervention (CSI), an intervention-based framework that measures each channel’s contribution by suppressing it during decoding and observing the change in the model’s predictions. This goes beyond attention weights, which show where a model looks but not what it depends on. Across two American Sign Language (ASL) datasets, we find that hand streams drive content-word prediction, while face and body streams contribute in a more variable, context-dependent way, with no single stream consistently dominating during question generation. Attention and routing scores offer a partial signal, but targeted interventions, particularly in later decoding layers, reveal functional stream use more faithfully.