Selecting optimal features in high-dimensional spaces remains challenging due to their complexity and the focus on correlational rather than causal relationships. While evolutionary computation algorithms for feature selection show promising results, they often face challenges in identifying feature subsets that are both interpretable and causally relevant. In this paper, we present a preliminary study in which we investigate how causality affects the search capability of feature selection based on evolutionary computation. We tested Genetic Algorithms and Particle Swarm Optimization to this aim and compared their performance with wrapper-based approaches. Comprehensive experiments across multiple benchmark datasets reveal that our methods consistently identify features with stronger causal relationships and superior interpretability than traditional approaches. Our results demonstrate the significant potential of integrating causality to enhance evolutionary computation algorithms for feature selection.