Effective decision-making in complex environments requires discerning the relevant from the irrelevant, a challenge that becomes pronounced with large multivariate time-series data. However, existing feature selection algorithms often suffer from complexity and a lack of interpretability, making it difficult for decision-makers to extract value, manage risks, and adhere to compliance regulations in an easily explainable manner. To address these challenges, we propose a novel causality-based feature selection technique that embeds an explainable unsupervised feature selection algorithm. We refer to our proposed method as Causal Feature Selection with Minimum Redundancy (CFSMR). Our method yields a minimum viable feature set without compromising model performance while ensuring interpretability. We conduct a large-scale experimental study to compare the proposed technique with conventional feature selection techniques. Our results demonstrate that our proposed method outperforms or performs on par with existing techniques, making it a promising approach for decision-makers seeking an effective and interpretable feature selection method.