Automobile sales analysis provides an important basis for production planning, procurement, supply-chain coordination, inventory management, and dealership resource allocation. However, dealership sales are jointly influenced by operational practices, market conditions, and regional policies. Conventional machine-learning models may have difficulty capturing spatial heterogeneity, maintaining predictive performance under regional sample imbalance, and providing interpretable evidence for managerial analysis. To address these issues, this study constructs a multi-source automotive dealership dataset based on automaker marketing records and proposes CDMEP, a multi-paradigm framework integrating spatial diagnosis, ensemble transfer learning, predictive interpretation, and assumption-conditional structural diagnosis. The framework comprises a Spatial Heterogeneity Decoupling Diagnostic Model (SHDM) for examining spatial clustering and regional differences; a Regional Adaptive Stacked Learner (RASL) combining balanced cross-regional transfer, TPE-tuned base learners, and constrained ensemble weighting; and a Causally Scrutable Optimization Policy (CSOP) incorporating SHAP-, GAM-, and PDP-based predictive interpretation, temporally ordered LiNGAM diagnosis, edge-level stability and sensitivity analysis. The results indicate the following: (1) dealership sales exhibit spatial clustering and regional heterogeneity in variables related to lead generation, customer conversion, and dealership service capability; (2) RASL achieves an R² of 0.875 on the independent test set, corresponding to an absolute improvement of 0.051 over the second-best benchmark in the present dataset; and (3) under the specified preprocessing procedures and LiNGAM assumptions, lead volume, customer traffic, and conversion rate repeatedly appear in candidate directional structures associated with next-month sales, although their estimated coefficients and diagnostic stability vary across regions. These findings provide preliminary evidence that CDMEP may serve as a useful analytical framework for regionally differentiated sales prediction and assumption-aware dealership analysis.