Causal Insight Analysis for doubleml-pipeline Outputs

The pipeline has already estimated debiased causal effects with Double Machine Learning (DML). Read the completed output_dir files directly using the exact metric definitions in references/metric_formulas.md — never refit raw regressions on the input panel.

Adapt to the run's schema (metadata.json first): Treatments, channels, interventions, outcome KPIs, entity dimensions (geo_col, item_col), and covariates vary across users and domains (including non-monetary treatments with no spend column). Derive every lever name, unit type, segment label, and driver strictly from metadata.json and the columns present in consolidated_1a_full_df.csv — never assume specific channels, treatments, or domain constructs that are not in the data. When a question cannot be answered from the run's outputs, say so explicitly and name the exact pipeline setting (treatment_cols, x_cols_list, covariates_for_cate_list, geo_col / item_col, optimize=True, or run_sensitivity=True) needed to answer it.