  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.
