Refactored OLS pipeline for interpretable coefficient export

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Refactored OLS pipeline for interpretable coefficient export

  • Day: 2026-06-11
  • Time: 11:45 to 11:55
  • Project: Dev
  • Workspace: WP 1: Strategic / Growth & Development
  • Status: Completed
  • Priority: HIGH
  • Assignee: Matías Nehuen Iglesias
  • Tags: Ols, Coefficients, Fixed-Effects, Diagnostics, Feature-Engineering, Interpretability

Description

Session Goal

Improve the OLS experiment stack so substantive interpretation can proceed from a scientifically interpretable design matrix and a complete coefficient export, rather than relying on diagnostics that were still missing transformed artifacts.

Key Activities

  • Audited the OLS notebook and backend pipeline to identify why interpretation was blocked.
  • Traced the gap to the model/diagnostics boundary: fitted models existed, but transformed coefficient tables were not being exported where interpretation notebooks could consume them.
  • Reviewed feature typing and found that interpretation was being weakened by relying on pandas dtypes instead of an explicit feature contract.
  • Reworked preprocessing and experiment-frame metadata to distinguish numeric, binary, categorical, and fixed-effect features.
  • Added richer artifact exports, including contextual prediction columns, transformed coefficient diagnostics, reference-level handling for one-hot encoded variables, and group residual summaries.
  • Updated experiment and diagnostics registry flow so interpretation outputs are plan-gated and aligned with the intended benchmark policy.

Achievements

  • The pipeline now supports feature-type-aware preprocessing and metadata propagation.
  • Coefficient diagnostics and contextual prediction artifacts were added to the export path.
  • Diagnostics execution was migrated successfully and validated via syntax checks.
  • The experiment suite was conceptually reorganized into a clean benchmark plus adjusted models with fixed effects and sensitivity/context variables separated more clearly.

Pending Tasks

  • Run the updated OLS notebook against the new exported coefficient table and verify the interpretation workflow end-to-end.
  • Confirm reference-level artifacts are complete for all one-hot encoded fixed effects.
  • Validate that the clean benchmark and geo/time-adjusted benchmark produce the expected comparative interpretation outputs.
  • Review any remaining integration work between experiments.py, the pipeline, and downstream reporting notebooks.

Evidence

  • source_file=2026-06-11.sessions.jsonl, line_number=9, event_count=0, session_id=9ee365faffe27ed27e0bc388ac92969ff64d3a8a24036e7528567c1934c8f9c4
  • event_ids: []

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