This study aims to estimate the causal effects of changes in elemental composition on the hardness in high-entropy alloys (HEAs) through the application of Double Machine Learning (DML). By integrating machine learning with causal inference, we quantify the individual impact of each element on the hardness of HEAs. Our approach leverages advanced algorithms such as Random Forest, XGBoost, Support Vector Regression (SVR), and Ridge Regression to effectively control for confounding factors, thereby ensuring robust causal estimation. The models are trained and validated on a curated dataset of HEA samples, allowing for accurate inference of elemental contributions under varying compositional regimes. The estimated effects indicate that increasing valence electron concentration, mixing enthalpy, and atomic size difference by one unit decreases hardness by 184.29, 3.38, and 6.4 HV, respectively, whereas increasing electronegativity difference and mixing entropy by one unit increases hardness by 742.42 and 44.37 HV, respectively. This methodological framework not only enhances predictive accuracy but also provides scientifically interpretable insights into the causal relationships between alloying elements and mechanical properties—offering valuable guidance for the rational design of high-performance materials.