Working Paper: NBER ID: w28293
Authors: Jeffrey Grogger; Sean Gupta; Ria Ivandic; Tom Kirchmaier
Abstract: We compare predictions from a conventional protocol-based approach to risk assessment with those based on a machine-learning approach. We first show that the conventional predictions are less accurate than, and have similar rates of negative prediction error as, a simple Bayes classifier that makes use only of the base failure rate. Machine learning algorithms based on the underlying risk assessment questionnaire do better under the assumption that negative prediction errors are more costly than positive prediction errors. Machine learning models based on two-year criminal histories do even better. Indeed, adding the protocol-based features to the criminal histories adds little to the predictive adequacy of the model. We suggest using the predictions based on criminal histories to prioritize incoming calls for service, and devising a more sensitive instrument to distinguish true from false positives that result from this initial screening.
Keywords: No keywords provided
JEL Codes: K14; K36
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| conventional risk assessment predictions (D81) | accuracy of predictions (C52) |
| Bayes classifier (C11) | accuracy of predictions (C52) |
| machine learning algorithms (C45) | prediction outcomes (C52) |
| historical data (Y10) | prediction accuracy (C52) |
| protocol-based features (C90) | prediction efficacy (C53) |