Robust multimedia systems should rely on salient features whose influence remains stable under contextual changes, but most robustness metrics focus on overall performance and overlook which features truly determine the model’s decisions. We propose FACER, a framework for feature-level robustness evaluation and enhancement based on causal effects. Specifically, we use SHAP to anchor salient features and construct a compact domain-specific causal graph. Based on causal do interventions, completeness—the ability to preserve the original decision using only the salient features—is measured via the Average Treatment Effect (ATE), while consistency—the stability of the decision under background perturbations when salient features are fixed—is measured via the Controlled Direct Effect (CDE). These metrics together form feature-level robustness measures. These feature-level robustness metrics are then transformed into training objectives by minimizing completeness and consistency gaps, forming a closed “evaluation–enhancement” loop. Experiments on speech emotion recognition and text classification tasks demonstrate that FACER can assess and enhance the robustness of important features at the sample level while maintaining task performance. FACER effectively reduces gaps in completeness and consistency, generalizes across modalities, and provides a practical, causal effect driven approach for building reliable and explainable multimedia models.