Causal risk models of air transport - NLR-ATSI

The difference between singular and generic causal relations is particularly important when
causation is used to determine responsibility or guilt, but that is not the purpose of the
causal risk models that are subject of this thesis.
The other way around, the absence of a singular cause effect relation is not sufficient to
negate a generic causal relation. A 95 year old heavy smoker without health problems is not
a proof that smoking is not harmful. Yet such singular examples may be compelling and
people’s faith in causal risk models may very well be eroded if they happen to know a few
of such singular examples that are seemingly contradictory to the generic model relations.
A risk model that not only explicitly represents failure scenarios but also success scenarios
would be helpful in this respect.
3.5. Strong and weak causal relations
Another potential source of confusion is the difference between weak and strong
relationships. In some cases, cause effect relations can be considered strong, in the sense
that the occurrence of the cause will always result in the effect. An example is the cause
‘empty fuel tank’ and the effect ‘car does not start’. Other cause-effect relations may be
weaker. An example is the cause ‘cigarette smoking’ and the effect ‘lung cancer’. The fact
that someone has a habit of smoking cigarettes does not always results in the person
developing lung cancer, although the probability for lung cancer is greatly increased when
someone smokes. The ‘weakness’ of the cause-effect relation depends on the conditional
probability P (effect | cause). If this conditional probability is 1 or close to 1 (as in the case
of the car example) the cause-effect is strong. If the conditional probability is close to 0, the
relation is weak. A weak statistical association should however not be taken as indicating a
non-causal relationship. Weak statistical associations may be seen for instance if an effect
is common and there are numerous possible causal pathways towards that effect.
A weak cause-effect relationship does not require high probabilities to be acceptable. What
is important is the statistical relevance of the relationship. When a certain drug increases the
recovery rate from 5% to 10% it is the statistical significance of the difference between the
treatment group and the control group that determines whether we accept that there is proof
that the drug is working.
3.6. The beginning and the end of causation
An obvious question to ask when the cause of an effect has been determined is “What
causes the cause?” This question is not only interesting from a philosophical point of view
but is also relevant for accident prevention. If the cause of an accident is determined to be
pilot fatigue this is interesting to know but in order to prevent similar occurrences from
happening we usually need to know what caused the fatigue, i.e. what caused the cause 14 .
Only then are we able to take measures to safeguard against similar events. If we are
sufficiently clever and persevering we can continue determining the causes of causes right
up to the big bang that created the universe. In a similar fashion we can think of the effects
of effects. In the event of an aircraft crash an immediate effect may for instance be the
release of toxic substances into the atmosphere if the aircraft was carrying hazardous
materials as cargo. This may then cause, perhaps even years later, health problems for
people exposed to those materials. Causal chains never really end, but for practical
purposes the model must.
14
Unless we can detect the presence of this proximal cause and take preventive measures
on it, i.e. in this case detect fatigued pilots and prevent them from flying.
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