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Machine Learning Can Predict Shooting Victimization Well Enough to Help Prevent It

Author

Listed:
  • Sara B. Heller

    (University of Michigan and NBER)

  • Benjamin Jakubowski

    (New York University)

  • Zubin Jelveh

    (University of Maryland)

  • Max Kapustin

    (Cornell University and NBER)

Abstract

Using Chicago police data, we train a machine learning model to predict the risk of being shot in the next 18 months. Out-of-sample accuracy is strikingly high. A central concern with using police data is “baking in” bias, or overestimating risk for groups likelier to interact with police conditional on behavior. Our predictions, however, accurately recover risk across demographic groups. Legal, ethical, and practical barriers should prevent using victimization predictions to target law enforcement. But using them to target social services could increase both the potential for interventions to reduce shootings and the available statistical power to detect those reductions.

Suggested Citation

  • Sara B. Heller & Benjamin Jakubowski & Zubin Jelveh & Max Kapustin, 2026. "Machine Learning Can Predict Shooting Victimization Well Enough to Help Prevent It," The Review of Economics and Statistics, MIT Press, vol. 108(4), pages 881-896, July.
    • Handle: RePEc:tpr:restat:v:108:y:2026:i:4:p:881-896
    DOI: 10.1162/rest_a_01519
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