Feature selection is a critical step in machine learning, particularly when dealing with high-dimensional datasets that contain redundant or irrelevant features. Traditional feature selection methods rely on correlation-based selection, which can misidentify important features due to spurious relationships rather than true causal effects. In this paper, we introduce Causal-SHAP, a novel feature selection method that integrates explainability with causal analysis. Our approach combines SHAP values to measure feature importance and causal analysis to determine which features have impact on predictive performance and a genuine causal impact on the target variable. By merging these two techniques, Causal-SHAP reduces dataset complexity while keeping only the most meaningful features, leading to better model accuracy and interpretability. The experimental validation on diverse datasets and machine learning tasks demonstrates that our method outperforms conventional feature selection techniques. Our findings underscore the importance of causality in feature selection, paving the way for more robust and trustworthy machine learning models.