Causal risk models of air transport - NLR-ATSI

with which aviation accidents are currently investigated, it seems highly unlikely that a
causal model would be able to add anything useful here. 15 However, it could order and
organize the results across many accidents and that is essentially useful in support of the
forward reasoning.
Causal models for forward reasoning are applied in a wide range of sciences, including
meteorology, finance and engineering. Models for risk assessment are examples of causal
models for forward reasoning. As will be shown in the next sections, there is a need for
such causal risk models for air transport safety. Therefore in the remainder of this thesis the
scope will be limited to causal models for forward reasoning. Most importantly, there is
need for a special case of forward reasoning; predicting the effect of changes. This has
added complications because it may require the potential to change the model, thus setting
requirements for the dynamics of the model.
3.8. Conclusions for this section
For the purpose of this thesis, the following definition of a causal model was adopted:
A causal model is a mathematical object that provides an interpretation and computation of
causal queries about the domain [Galles & Pearl 1998]. A causal model can be associated
with a directed graph in which each node corresponds to a variable of the model and the
directed edges (arrows) point from the variables that have direct influence to each of the
other variables that they influence.
Usability requirements of the model determine whether statistical associations alone can be
used to construct the model. If the purpose of the model is to predict, then a model based on
statistical associations can be useful. But in our case the objective of the model is to
intervene and statistical associations alone are not sufficient, there has to be an underlying
assumption, a causal assumption. This causal assumption should be based on deeper
knowledge on cause and effect through theoretical studies and accumulated knowledge.
Statistical associations should be used carefully, as demonstrated by Simpson’s paradox
and the examples of controversies related to interpreting statistical associations. It is
therefore essential for developers of a causal model of aviation safety to have substantial
knowledge on relevant aspects of aviation, including technology, operations, regulation and
procedures for the complete lifecycle.
What is actually the cause or causes of an event is not always unambiguous. To a certain
extent, it is an assumption that has to be agreed upon by those who work with it. The
agreement is necessary because the assumption has consequences. It is therefore necessary
that the underlying assumptions are transparent. Causal chains never really end, but for
practical purposes the model must. In practice a useful criterion for the extent of the causal
risk model is that it will have to include decisions and actions up to the highest managerial
level of the actors that are directly involved.
Part of the complexity of causal relations is their apparent probabilistic nature. We often
arrive at generic causal relations by generalising from individual cases of occurrence and
then apply this general knowledge to other individual occurrences. But evidence of a
generic causal relationship is not sufficient to prove a singular causal relationship, and the
absence of a singular cause effect relation is not sufficient to negate a generic causal
relation. This can lead to confusion and can seriously erode the confidence in causal risk
15
Aircraft accident investigation is a process that takes approximately 2 years to complete
and involves dozens of people, many of them working almost full time on a single accident.
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