Contextual bandit policy release review project

Freeze the decision record

Specify context available at intake, eligible lanes, logging-policy version, selection probability and the reward contract. For every decision, save the chosen action and immutable case ID before later outcomes arrive. The logging lesson defines the minimum record. Exclude any action that safety or service rules make ineligible.

Check identification before value

Audit support for every candidate action and context. Compute raw inverse-propensity value, largest weights and effective sample size; compare with an independently fitted augmented propensity estimate. A model-only estimate and a weighting estimate that sharply disagree call for investigation, not a favorable-number choice. IPS and augmented evaluation expose distinct failures.

Wait for a common reward clock

Define when the handoff result is mature, report pending and missing outcomes by action, and retain staffing cost in the reward. A quicker proxy may help monitoring but cannot silently replace the final target. Reward maturity supplies the eligibility rule.

Plan the bounded live comparison

Offline estimates screen candidates; they do not prove how the challenger would affect outcomes once its actions change the system. Review exploration eligibility, per-site capacity, stop conditions, monitoring ownership and rollback. If a prospective randomized comparison is authorized, preserve its assignment and actual action logs. The exploration guide keeps probabilities and constraints aligned.

Record a decision, not a metric alone

The code checks whether an evidence packet contains necessary artifacts; it cannot certify a good policy. Attach numerical estimates with uncertainty, sample support, group-level harms and an explicit owner for the pilot. If any condition is missing, hold the release discussion rather than treating it as passed.

Implementation

def bandit_review_gate(packet):
    requirements = {
        "action_support_checked": "action support",
        "propensities_logged": "logged probabilities",
        "reward_maturity_audited": "reward maturity",
        "weight_concentration_reported": "weight concentration",
        "group_harms_reviewed": "group harms",
        "capacity_guardrail_set": "capacity guardrail",
        "rollback_owner_named": "rollback owner",
    }
    blockers = [label for key, label in requirements.items() if not packet.get(key)]
    return "eligible for pilot review" if not blockers else "hold: " + ", ".join(blockers)

rapid_lane_packet = {
    "action_support_checked": True,
    "propensities_logged": True,
    "reward_maturity_audited": False,
    "weight_concentration_reported": True,
    "group_harms_reviewed": True,
    "capacity_guardrail_set": False,
    "rollback_owner_named": True,
}
assert bandit_review_gate(rapid_lane_packet) == (
    "hold: reward maturity, capacity guardrail"
)

Performance and operating cost

The checklist is O(K) for K requirements. Offline IPS and augmented propensity scoring each scan N mature decisions in O(N) time. The true operating cost lies in safe randomized support, delayed reward collection, candidate serving, monitoring and the ability to reverse a harmful policy.

Common Mistakes

• Do not promote a candidate because one offline estimator looks favorable.
• Do not evaluate an action without logged support or a valid selection probability.
• Do not call a pending outcome a failure or omit capacity from the reward contract.

Read next

• Contextual bandit action logging and support
• Inverse-propensity policy value
• Augmented propensity policy value
• Exploration budget and action guardrails
• Delayed bandit rewards and outcome maturity
• Group error gaps and policy audit

Continue the workflow: Transfer learning release review project.

Continue the workflow: Sequential decision state and reward contract.