Causal risk models of air transport - NLR-ATSI

Confusion between causation and statistical associations
Post war diseases
War syndromes have been associated with armed conflicts at least since the U.S. Civil War
(1861-1865) but research efforts to date have been unable to conclusively show causality
[Hyams et al 1996, Jones et al 2002]. Explanatory causes that were proposed have varied
from the heavy marching packs compressing the chest (U.S. Civil War 1861-1865, Boer
War 1899-1902) to concussion from modern weapons (World War I 1914-1918), the use of
Agent Orange (Vietnam War 1957-1975) and the use of depleted uranium in armour
penetrating ammunition (Persian Gulf War 1991).
Health effects of electromagnetic fields
Concern about possible adverse health effects from exposure to extremely low-frequency
electric and magnetic fields (EMF) emanating from the generation, transmission and use of
electricity was first brought about by an epidemiologic study concerning a relation between
risk of childhood leukaemia and exposure to EMF [Wertheimer & Leeper 1979]. But a
more recent review of the epidemiologic literature on EMF and health concludes that in the
absence of experimental evidence and given the methodological uncertainties in the
epidemiologic literature, there is no chronic disease for which an explanatory relation to
EMF can be regarded as established [Ahlbohm et al 2001].
These examples demonstrate why inferring causal relations requires subject-specific
background knowledge. It is therefore essential for developers of a causal model of aviation
safety to have substantial knowledge on all relevant aspects of aviation, including
technology, operations, regulation and procedures for the complete lifecycle. Absence of
subject matter knowledge will lead to causal risk models that produce misleading results.
Going back to the example of post war diseases, it is easy to imagine how ineffective or
even counterproductive treatments will be prescribed if any one of the above mentioned
cause effect relations are reproduced alone in, say, a diagnostic model.
Indeed, statistical associations can be hopelessly confusing, as is illustrated by Simpson’s
paradox. Simpson’s paradox refers to the phenomenon whereby an event C increases the
probability of E in a given population p and, at the same time, decreases the probability of
E in every subpopulation of p. For example, in Table 1, if we associate C (connoting cause)
with taking a drug, E (connoting effect) with recovery, then the drug seems to have no
beneficial effect on both males and females compared to no drug and yet is beneficial to the
population as a whole.
Table 1: Simpson’s paradox.
Combined Effect No effect Recovery rate
Drug 20 20 50%
No drug 16 24 40%
Males Effect No effect Recovery rate
Drug 18 12 60%
No drug 7 3 70%
Females Effect No effect Recovery rate
Drug 2 8 20%
No drug 9 21 30%
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