Comparing Conventional and Machine-Learning Approaches to Risk Assessment in Domestic Abuse Cases

70 Pages Posted: 5 Jan 2021

See all articles by Jeffrey Grogger

Jeffrey Grogger

University of Chicago - Harris School of Public Policy; National Bureau of Economic Research (NBER)

Sean Gupta

London School of Economics & Political Science (LSE) - London School of Economics

Ria Ivandic

London School of Economics & Political Science (LSE) - Centre for Economic Performance (CEP); King's College London - Department of Political Economy

Tom Kirchmaier

London School of Economics - Centre for Economic Performance; Copenhagen Business School

Multiple version iconThere are 3 versions of this paper

Date Written: January 4, 2021

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.

JEL Classification: K14,K36

Suggested Citation

Grogger, Jeffrey T. and Gupta, Sean and Ivandic, Ria and Kirchmaier, Tom, Comparing Conventional and Machine-Learning Approaches to Risk Assessment in Domestic Abuse Cases (January 4, 2021). University of Chicago, Becker Friedman Institute for Economics Working Paper No. 2021-01, Available at SSRN: https://ssrn.com/abstract=3760094 or http://dx.doi.org/10.2139/ssrn.3760094

Jeffrey T. Grogger (Contact Author)

University of Chicago - Harris School of Public Policy ( email )

1155 East 60th Street
Chicago, IL 60637
United States

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Sean Gupta

London School of Economics & Political Science (LSE) - London School of Economics

United Kingdom

Ria Ivandic

London School of Economics & Political Science (LSE) - Centre for Economic Performance (CEP) ( email )

Houghton Street
London WC2A 2AE
United Kingdom

King's College London - Department of Political Economy ( email )

Strand Building
London
United Kingdom

Tom Kirchmaier

London School of Economics - Centre for Economic Performance ( email )

Houghton Street
London, WC2A 2AE
United Kingdom
+44 207 955 6854 (Phone)

HOME PAGE: http://sites.google.com/site/tomkirchmaier/home

Copenhagen Business School ( email )

Solbjerg Plads 3
Frederiksberg C, DK - 2000
Denmark

Do you want regular updates from SSRN on Twitter?

Paper statistics

Downloads
30
Abstract Views
290
PlumX Metrics