The role of collider bias in understanding statistics on racially biased policing

TL;DR

This paper explains how collider bias can distort conclusions about racial bias in police shootings, using causal Bayesian networks to clarify the true nature of the data and bias.

Contribution

It introduces a causal Bayesian network model to illustrate collider bias in police encounter data and demonstrates how it affects interpretations of racial bias.

Findings

Collider bias can mask genuine racial bias in police shooting data.

Causal Bayesian networks help clarify and differentiate bias explanations.

Different conclusions can arise from the same data due to collider bias.

Abstract

Contradictory conclusions have been made about whether unarmed blacks are more likely to be shot by police than unarmed whites using the same data. The problem is that, by relying only on data of 'police encounters', there is the possibility that genuine bias can be hidden. We provide a causal Bayesian network model to explain this bias, which is called collider bias or Berkson's paradox, and show how the different conclusions arise from the same model and data. We also show that causal Bayesian networks provide the ideal formalism for considering alternative hypotheses and explanations of bias.