In meta-learning tasks with tabular data, the choice of meta-features significantly impacts model performance and interpretability. This study investigates the necessity and methods of meta-feature selection in the context of meta-learning, particularly for tabular datasets. We address the fundamental question: Is it better to select a subset of meta-features or use the entire feature set? We examine various selection techniques, including filter, wrapper, and embedded methods, as well as a novel causal-based approach utilizing counterfactual reasoning. Our experiments demonstrate that feature selection generally enhances performance, with causal-based methods, especially those leveraging counterfactual generation, showing superior efficiency and generalizability. Furthermore, we explore how these methods fare under shifts in data, particularly when non-informative features are added. The results reveal that the counterfactual method maintains high efficacy across different meta-learners and exhibits a favorable balance between model performance and interpretability. These findings underscore the importance of meta-feature selection in improving the adaptability and transparency of meta-learners for tabular data tasks. Code and supplementary materials for this research are available on GitHub: https://github.com/ITMO-NSS-team/MetaSelect .