  We consider the problem of assessing whether, in an individual case, there is
a causal relationship between an observed exposure and a response variable.
When data are available on similar individuals we may be able to estimate
prospective probabilities, but even under ideal conditions these are typically
inadequate to identify the "probability of causation": instead we can only
derive bounds for this. These bounds can be improved or amended when we have
information on additional variables, such as mediators or covariates. When a
covariate is unobserved or ignored, this will typically lead to biased
inferences. We show by examples how serious such biases can be.
