Predictive Modeling

Original name

Original description

Use whenever the GOAL is a prediction rather than an effect — predict, score, rank, flag, classify, forecast, or detect-anomalies on units to drive an action ("which pharmacy is likely diverting opioids from its shipments", "which claim to audit", "which account to review", "flag the outliers", "rank by risk", "who's likely to churn"). The third arm of the modeling fork — route here by GOAL, not algorithm: if the deliverable is a prediction it is this skill even when a random forest does the work; if the goal is a causal effect it stays in causal-identification EVEN when ML does the heavy lifting (double/debiased ML, causal forests, ML propensity scores). Forces the prediction workflow — write and sign off the Prediction Spec (decision + cost asymmetry, target/label + regime, prediction-time, features + leakage audit, deployment-matched split, metric-tied-to-decision + baseline, method) BEFORE fitting; PROVE the evaluation is honest (permutation/null leakage probe, deployment-mirroring holdout, calibration, beat-the-baseline) before trusting any metric; and never read feature importance / SHAP as causal. Covers clean-label, proxy/weak-label, unsupervised/anomaly (no label), and ranking/triage regimes, in R, Julia, or Python — a high validation score is not a working model until leakage is ruled out and the split mirrors deployment.