Project: audit a route-time regression before claiming a policy gain

Last updated: 7 Oct 20265 min read

project

Rebuild route-level evidence, check residual spread and depot dependence, and decide whether a policy coefficient is fit for release.

Freeze the question and population

A logistics group claims its revised dispatch policy saves time per route. Define eligible routes, clock boundaries, depot and calendar coverage, the policy assignment process, and whether the target is an association or a causal change. A regression of observed minutes on policy, distance, load and depot can support different claims under different assumptions. Preserve excluded and missing routes in an eligibility ledger. The regression-estimand lesson starts the review.

Inspect variance and influence

Plot residuals against predicted route time, distance and load. Record influence and influential observations, including unusually long rural routes. Compare the coefficient and uncertainty under the planned error model and a justified heteroskedasticity-aware calculation. Do not silently remove routes because they weaken a favorable policy result. The HC3 lesson explains why uncertainty can change while the coefficient does not.

Rebuild the independent-group story

Trace the policy rollout and shared shocks across depots and days. If the decision was made for each depot, parcel rows are not independent assignments. Count groups in each policy state and decide whether depot-level clustering or another design-aware method is supportable. A group with no comparison exposure can force extrapolation. The cluster lesson separates group uncertainty from observational assignment bias.

Publish a qualified packet

The packet includes cohort SQL, outcome and predictor timing, coefficient units, residual and influence summaries, influence sensitivity, independent-group counts, error-structure rationale, and uncertainty under prespecified alternatives. The gate below holds the policy claim if the independent unit is undefined or an influential route is hidden. Passing it allows review of the estimate, not automatic causal attribution. A follow-up randomized rollout may still be the right decision.

Implementation

def route_regression_gate(audit):
    if not audit["cohort_reconciled"]:
        return "hold:route-frame"
    if not audit["assignment_unit_defined"]:
        return "hold:independent-unit"
    if audit["unreviewed_influential_routes"]:
        return "hold:influence-review"
    if not audit["group_uncertainty_reviewed"]:
        return "hold:dependence"
    return "review:qualified-effect"

audit = {"cohort_reconciled": True, "assignment_unit_defined": False,
         "unreviewed_influential_routes": 0,
         "group_uncertainty_reviewed": True}
assert route_regression_gate(audit) == "hold:independent-unit"
assert route_regression_gate({**audit, "assignment_unit_defined": True})        == "review:qualified-effect"

Performance and operating cost

The gate is O(1); cohort and group reconciliation is O(n) expected time with indexed routes. Fitting and resampling a regression cost more, but the critical resource is independent rollout units. A fast row-level standard error cannot create depots that were not studied.

Common Mistakes

Calling an adjusted observational slope a causal policy effect.
Choosing the covariance method by whether the p-value crosses a threshold.
Dropping influential rural routes without showing the change in target population.
Clustering parcel rows at a unit smaller than the policy assignment.