The Big Picture
The use of force by law enforcement remains a contentious issue, with significant implications for public trust, policy reform, and officer accountability. By employing causal inference techniques, researchers can uncover the factors driving use-of-force incidents and assess the effectiveness of policy interventions. This study explores how causal inference methods, paired with robust data, provide deeper insights into the patterns, causes, and potential solutions for police use-of-force incidents.
Listen to Discussion About This Research
What We Did
Researchers applied advanced causal inference methodologies, such as propensity score matching and regression discontinuity designs, to analyze police use-of-force data. They utilized comprehensive datasets, including police record management systems, call-for-service logs, and body-worn camera footage. The study also integrated supplementary data from sources like the Census Bureau and victimization surveys to contextualize findings within demographic and situational frameworks.
What We Found
1
Disparities in Use of Force:
Significant racial disparities exist in the application of force, with Black civilians experiencing higher rates of force relative to their population size, as evidenced by Portland’s police data.
2
Impact of Policy Standards:
Departments with clear use-of-force policies and de-escalation training reported fewer excessive force incidents.
3
Data Limitations
Police records often focus on force used, underreporting encounters where force could have been used but wasn’t, which skews analysis.
4
Role of External Data:
Supplementary data sources, such as body-worn cameras and public surveys, enhance the reliability of causal inference methods and provide a fuller picture of use-of-force incidents.
Why This Matters
Understanding the drivers and outcomes of police use-of-force incidents helps to:
1
Identify and address systemic biases and disparities in policing.
2
Inform evidence-based policy reforms to reduce unnecessary and excessive force.
3
Enhance transparency and accountability in law enforcement practices.
What Can We Do?
To improve law enforcement outcomes and reduce the incidence of unnecessary force, we can:
1
Invest in Training:
Expand de-escalation and bias-awareness training for officers.
2
Enhance Data Transparency:
Mandate detailed and standardized reporting of all police encounters.
3
Leverage Technology:
Utilize body-worn cameras and AI tools to monitor officer behavior.
4
Foster Community Engagement:
Involve community stakeholders in policy development and evaluation.
The takeaway?
Causal inference techniques offer a powerful framework for understanding and mitigating police use-of-force incidents. By integrating diverse data sources and addressing biases in reporting, law enforcement agencies can develop policies that enhance public trust, ensure officer accountability, and improve community safety.
About the Study
This research was published in CHANCE in December 2024.
APA Citation:
Bourgeois, J. W., Haensch, A., Kher, S., Knox, D., Lanzalotto, G., & Wong, T. A. (2024). How to use causal inference to study use of force. CHANCE, 37(4), 6–10. https://doi.org/10.1080/09332480.2024.2434435