  The plethora of algorithms in the research field of process mining builds on
directly-follows relations. Even though various improvements have been made in
the last decade, there are serious weaknesses of these relationships. Once
events associated with different objects that relate with a cardinality of 1:N
and N:M to each other, techniques based on directly-follows relations produce
spurious relations, self-loops, and back-jumps. This is due to the fact that
event sequence as described in classical event logs differs from event
causation. In this paper, we address the research problem of representing the
causal structure of process-related event data. To this end, we develop a new
approach called Causal Process Mining. This approach renounces the use of flat
event logs and considers relational databases of event data as an input. More
specifically, we transform the relational data structures based on the Causal
Process Template into what we call Causal Event Graph. We evaluate our approach
and compare its outputs with techniques based on directly-follows relations in
a case study with an European food production company. Our results demonstrate
the benefits for enriching process mining with additional knowledge from the
domain.
