The role of collider bias in understanding statistics on racially biased policing
This addresses methodological issues in social science research on policing bias, though it is incremental in applying existing causal frameworks to a specific problem.
The paper tackled contradictory conclusions about racial bias in police shootings by identifying collider bias in data limited to police encounters, using a causal Bayesian network model to explain how the same data can lead to different interpretations.
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.