Advanced Interpretable Machine Learning Pipeline

Develop a comprehensive machine learning interpretability framework that provides deep insights into model decision-making processes. Implement multiple interpretation techniques including SHAP values, LIME, partial dependence plots, and causal attribution methods. Create a modular system that can generate human-readable explanations, detect potential biases, and provide granular feature importance across different model architectures. Include automated reporting and visualization of model internals.