Build an Eligibility-Cutoff Impact Analysis Workbench

A researcher evaluating a threshold-based program.

Investigate outcomes around a policy eligibility threshold. Expose bandwidth choices, assignment irregularities, and the local scope of any estimated discontinuity.

Key: unit_id + assignment_period.

Fields: running_variable, cutoff, eligibility, treatment_received, outcome, cluster_id.

Do not confuse assignment with receipt. State sharp or fuzzy design before estimation.

Inspect assignment and running-variable density near the cutoff.

Fit prespecified local models with bandwidth sensitivity.

Separate outcome discontinuity from treatment discontinuity.

Export local estimates, diagnostics, and extrapolation limits.

A cutoff plot, bandwidth controls, assignment diagnostics, and an analysis decision log.

A reproducible local-estimation pipeline.

A policy memo stating the population near the cutoff and unresolved threats.

Runnable local application, setup commands, and a dependency lockfile.

Versioned input data, data dictionary, acquisition instructions, and rights notes.

Calculation traces, source references, assumption records, and exported review artifacts.

A deterministic baseline and tests for normal, incomplete, contradictory, and failure-recovery cases.

A recorded complete workflow with a reproducible manifest and explicit limitations.

The displayed effect is local, not a population-wide average.

A weak treatment discontinuity prevents an unqualified fuzzy-design estimate.

Bandwidth exploration is disclosed rather than selected only for significance.

Predetermined covariates and density diagnostics are reported.

A positive outcome jump with a near-zero treatment jump cannot support a stable treatment-effect ratio.

The attached starter files are prototype inputs. Expected starter answers are visible. They are not independent holdouts.

Full target: Generate a synthetic running variable, cutoff, treatment receipt, and outcomes with known local effects. Include manipulated and noncompliant assignment variants.

Complete target contracts and expert review remain pending. Public observations are not historical release vintages.

statsmodels, SciPy, Plotly

Use an available model appropriate to the task. Record provider, model identifier, version or retrieval date, settings, cost, and latency. Use deterministic code for calculations. Models may extract, retrieve, or explain evidence. They must not invent missing records, override approval rules, or claim professional authority.

The fixture threshold is a fictional diagnostic rule, not a universal statistical standard.

No fabricated records, source citations, professional endorsements, or expert-adjudication claims.

No future-data leakage into dated decisions, model fitting, or historical valuation inputs.

No unauthorized payments, account restrictions, regulatory filings, or production changes.

No automated appraisal certification, tax-compliance determination, or unsupported fraud accusation.

No concealed uncertainty or silently invented inputs required for a numerical conclusion.

Correct domain calculations and justified methods with documented assumptions. Weight: 30.

Source lineage, temporal integrity, rights checks, and explicit synthetic-data labels. Weight: 25.

A working application with review, export, error handling, and reproducible state. Weight: 20.

Adversarial cases, uncertainty, missing-data behavior, and sensitivity analysis. Weight: 15.

Evidence-linked conclusions that distinguish facts, assumptions, and unresolved judgments. Weight: 10. Score each dimension from zero to two. Weighted score = sum(weight * score / 2). A failed safety gate blocks acceptance.