The need for causal models to understand and explain whether statistics provide evidence of racially biased policing

Even before the recent George Floyd case, there has been much debate about the extent to which claims of systemic racism are supported by statistical evidence. 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.

In this short paper 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.

Fenton N.E., Neil M, Frazier S (2020), "The role of collider bias in understanding statistics on racially biased policing", http://arxiv.org/abs/2007.08406