hs_explain_levers

net.hunter-seeker/hunter-seeker · Verify this server

Free; no engine run. For one or more entities already ranked by hs_rank_topk, compute the minimal set of feature changes (counterfactual levers) that would move the entity out of the high-risk / high-likelihood pattern. Best for: "what would have to change for this customer not to churn", "what's driving this risk", "how do I intervene". Not recommended for: entities not present in a prior ranking; guarantees of real-world causal effect (these are minimal model-based flips, not proven interventions). Returns: per-entity minimal feature changes (each a feature label + a direction: increase / decrease / change), a coarse magnitude (substantial / notable / slight), and likelihood_direction (lower / higher / unchanged) - which way the change moves PREDICTED LIKELIHOOD of the outcome - plus provenance; every lever is labeled association_not_causal. likelihood_direction is a FACT, not a recommendation, and it is NOT fixed to "lower" - READ IT PER LEVER. Which way it reads follows the polarity the engine resolved for this outcome: on an ADVERSE outcome (churn, default, failure) the levers move an entity OUT of the high-likelihood pattern and read "lower"; on a DESIRABLE outcome (converted, renewed, closed) the engine returns COMPLETION levers that move an entity INTO it, and those read "higher". Assuming "lower" on a desirable outcome inverts every lever you present. Whether the direction you get is the direction you want depends on whether the outcome is desirable (converted, renewed, closed) or adverse (churn, default, failure) - you know which, and Hunter-Seeker does not infer it. Decide the good/bad reading yourself, or ask the user, before presenting a lever as an improvement. No raw scores, score deltas, thresholds, or weights are returned - these are coarse, model-associated flips, not causal guarantees. Common mistakes: interpreting levers as causal guarantees - present them as "what the model associates with a different outcome", especially in regulated or person-level domains.