To predict the mechanical properties of high-entropy alloys by machine learning, this study aimed to solve the quantitative challenge that the independent contribution of a single parameter to process control is difficult to isolate from correlated models. The core challenge of identifying quantitative causal effects lies not only in accurately identifying key factors but also in quantifying the heterogeneity of these effects as they vary with respect to other factors. A quantitative causal effect analysis framework that integrates physics-based feature engineering, machine learning modelling, interpretable correlation analysis, and causal inference was proposed in this study, enabling accurate prediction of the mechanical properties of high-entropy alloys and quantitative characterization of heterogeneous causal effects. By constructing physical descriptors, the prediction accuracy is significantly enhanced, and the key factors influencing the predictions are identified as the cold rolling reduction (CR) and the annealing temperature (AT). Ultimately, the quantitative causal effects of the two factors on the mechanical properties are obtained: for a 10% increase in the CR, the ultimate tensile strength varies between -13.0 and 92.0 MPa (with an average change of 50.1 MPa), and the elongation varies between -5.02% and 0.62% (with an average change of -0.29%); for a 100 K increase in AT, the ultimate tensile strength varies between -164.9 and -57.2 MPa (with an average change of -1 10.6 MPa), and the elongation varies between 5.2% and 11.2% (with an average change of 8.3% ). The wide range of effects is determined by the heterogeneity arising from the combinations of other weakly correlated factors beyond the key factors, and the reliability of these effects is validated through other experimental observations.