Case study · Research engineering
An analysis should be
able to explain itself.
A reproducible, configurable research pipeline connecting source data, model outputs, and the numbers reported in a manuscript.
The problem
An empirical result has a long supply chain.
A change in a filter, a sample rule, or a specification can affect dozens of downstream tables and statements.
The thesis workflow addresses that problem with explicit configuration and automated reconciliation. The goal is to make the link between a reported number and the analysis that produced it easier to inspect.
The design
From source to checked statement.
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Construct
Clean and link two source extracts. Declare sample rules, dictionaries, and specifications in configuration.
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Estimate
Generate narrative features and model outputs from a consistent analytical dataset.
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Reconcile
Check sample counts against the construction waterfall and re-derive baseline estimates.
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Audit reporting
Compare numerical statements in the thesis with the computed results store.
Architecture overview based on the thesis workflow. No proprietary data or executable research code is embedded in this page.
Contribution
A repeatable process with explicit checks.
What the work involved
Python-based data construction, configurable feature definitions, model estimation, and automated checks for analytical and reporting consistency.
Why it is useful
It makes revisions easier to trace and numerical inconsistencies easier to catch. A public demonstration using synthetic data would be a natural extension of this work.