  The verification and validation of automated driving systems at SAE levels 4
and 5 is a multi-faceted challenge for which classical statistical
considerations become infeasible. For this, contemporary approaches suggest a
decomposition into scenario classes combined with statistical analysis thereof
regarding the emergence of criticality. Unfortunately, these associational
approaches may yield spurious inferences, or worse, fail to recognize the
causalities leading to critical scenarios, which are, in turn, prerequisite for
the development and safeguarding of automated driving systems. As to
incorporate causal knowledge within these processes, this work introduces a
formalization of causal queries whose answers facilitate a causal understanding
of safety-relevant influencing factors for automated driving. This formalized
causal knowledge can be used to specify and implement abstract safety
principles that provably reduce the criticality associated with these
influencing factors. Based on Judea Pearl's causal theory, we define a causal
relation as a causal structure together with a context, both related to a
domain ontology, where the focus lies on modeling the effect of such
influencing factors on criticality as measured by a suitable metric. As to
assess modeling quality, we suggest various quantities and evaluate them on a
small example. As availability and quality of data are imperative for validly
estimating answers to the causal queries, we also discuss requirements on
real-world and synthetic data acquisition. We thereby contribute to
establishing causal considerations at the heart of the safety processes that
are urgently needed as to ensure the safe operation of automated driving
systems.
