Anaerobic fermentation of organic solid waste (OSW) for lactic acid production represents an effective pathway for high-value resource recovery. However, conventional experience-based parameter regulation methods are limited in their ability to accurately characterize the coupled effects of multiple operational factors, often leading to extended fermentation periods, suboptimal parameter design, and unstable performance. To identify key control variables and quantitatively optimize process conditions, an interpretable causal machine learning (ICML) framework was applied to evaluate lactic acid production during the anaerobic fermentation of OSW. Three machine learning models—random forest (RF), gradient boosting regression (GBR), and extreme gradient boosting (XGBoost, XGB)—were systematically compared. The XGB model exhibited superior predictive performance in estimating lactic acid concentration, yield, and production rate. Based on the optimized XGB model, Shapley Additive Explanations (SHAP) were employed to quantify the relative importance of input variables. Hydraulic retention time (HRT), total solids (TS), volatile solids (VS), and organic loading rate (OLR) were identified as key factors influencing lactic acid production performance. To further elucidate causal relationships, the linear non-Gaussian acyclic model (LiNGAM) was used to distinguish direct and indirect causal effects between features and target variables. The results indicated that HRT exerted a significant direct effect on lactic acid yield, while OLR was the primary positive driver of the production rate. Additionally, substrate type and inoculum ratio regulated the process through direct or indirect pathways. Partial dependence plots (PDPs) were subsequently applied to determine optimal parameter ranges. Specifically, lactic acid concentration exceeded 20 g/L when VS > 50 g/L, TS was 200 – 250 g/L, HRT was 2.5 – 5.0 d, and OLR < 18 g VS/(L·d); lactic acid yield peaked above 0.30 g/g VS when HRT was 10 – 15 d, TS < 25 g/L, OLR < 10 g VS/(L·d), and temperature was 30 – 45 °C; and lactic acid production rate exceeded 7 g/(L·d) when OLR > 30 g VS/(L·d), HRT < 3 d, VS > 100 g/L, and TS > 130 g/L. Two-dimensional partial dependence analysis (2D-PDP) further demonstrated the interactive effects of HRT and TS, defining suitable operating windows for balancing concentration and production rate. These findings demonstrate that the ICML approach can effectively elucidate the nonlinear relationships between process parameters and lactic acid production performance, offering quantitative guidance for parameter optimization and experimental design in OSW-based lactic acid production, thereby contributing to the advancement of organic waste resource recovery.