Concrete compressive strength is traditionally modeled as a function of mixture composition, with cement dosage often assumed to produce proportional strength gains. However, such interpretations are typically correlational and do not quantify the causal effectiveness of cement additions under varying mixture conditions. This study introduces an interpretable causal machine learning (ICML) framework to estimate the marginal causal effect of cement dosage on compressive strength using an R-learner-based approach. Cement content is treated as a continuous intervention variable, and heterogeneous treatment effects are estimated conditionally on mixture composition and curing age. The estimated average marginal effect of cement dosage is 0.136 MPa per kg/m3 (95% bootstrap confidence interval: 0.1055, 0.1433). However, substantial heterogeneity is observed, with individual marginal effects ranging from −0.027 to 0.370 MPa (5th–95th percentile). Near-zero and, in limited regimes, negative marginal effects emerge under high water content and unfavorable mixture conditions, indicating inefficient cement utilization. Robustness checks across alternative cross-fitting schemes and trimming procedures confirm the stability of the estimated causal effects. Unlike conventional machine learning models that explain predicted strength values, the proposed framework applies explainability directly to the estimated causal effect function. Local SHAP-based explanations reveal the mixture configurations under which cement additions are effective or inefficient. By explicitly identifying mixture conditions under which cement additions are effective or inefficient, the proposed framework supports more rational cement use, reducing unnecessary material consumption, lowering construction costs, and easing the decision-making burden on designers in practical concrete mix design.
TÜYLÜ et al. (Sat,) studied this question.