- Causal inference refers to determining if an exposure caused an outcome based on evidence, rather than just an observed association. Establishing causation requires considering factors like temporality, strength of association, consistency of findings, and biological plausibility. - Hill's criteria and Rothman's causal pies model provide frameworks for systematically evaluating the weight of evidence supporting or refuting a causal relationship. Necessary causes must always be present to produce the outcome, while sufficient causes can single-handedly cause the outcome. - Distinguishing causal relationships from risk factors is important, as risk factors are not necessarily direct causes but may indicate underlying causal mechanisms. Experimental evidence can strengthen causal inferences but is often lacking for ethical or practical reasons

1. CASUAL INFERENCE
PAMELAH N. KIHEMBO

2. Outline
At the end of the lesson, you must be able to:
• Distinguish between association and a causal relationship.
• Describe and apply Hill's criteria and for a judgment of causality.
• Describe the sufficient-component cause model.
• Discuss in general the differences in the weight of evidence needed
for determining causality versus taking public health action.

3. Introduction
• refers to the process of drawing a conclusion about a causal
connection based on the conditions of the occurrence of an effect
(Holland 1986)
• The gold standard approaches for causal inference are randomized
experiments
• Relates exposures, risk factors, independent variables to outcomes,
effects, diseases, injuries, disabilities, death, e.t.c
• Differentiates statistical association Vs Biological causation: cause –
effect relationship

4. • Epidemiology is primarily focused on establishing valid associations
between 'exposures' and health outcomes.
• However, establishing an association does not necessarily mean that
the exposure is a cause of the outcome.
• Most definitions of "cause" include the notion that it is something
that has an effect or a consequence - causation

5. Causation History
• Historically, there have been many efforts to account for the occurrence of
disease outcomes. Religions often attributed disease outbreaks or other
misfortunes to divine retribution - punishment for mankind's sins.
Hippocrates promoted the concept that disease was the result of an
imbalance among four vital "humors" within us:
• Yellow Bile
• Black Bile
• Phlegm
• Blood
• Hippocrates believed that if one of the humors became excessive or
deficient, health would deteriorate and symptoms would develop.
Hippocrates was a keen observer and tried to relate an individual's
exposures (e.g., diet, exercise, occupation, and other behaviors) to
subsequent health outcomes.

7. CONT…
• Another popular theory that persisted until the end of the 19th century was
that miasmas were responsible for disease. Bad odors were equated with
disease.
• Miasmas were toxic vapors or gases that emanated from cesspools or swamps
or filth, and it was believed that if one inhaled the vapors, disease would
result. This theory provided an explanation for outbreaks of infectious
disease, including cholera and plague.
• As a result many ineffective interventions were pursued. Bonfires and smoking
urns were used to prevent both plague and cholera. In the 14th century
"plague doctors" wore masks with beak-like projections filled with aromatic
herbs in order to counteract the effect of miasmas.
• Echoes of the miasmatic theory can be found in the name "malaria", derived
from the Italian for "bad air" (mala, aria). It reflects the correct observation
that the disease was more common in swampy areas, but it misidentified the
cause as the foul odors rather than the bacterium caused by the mosquito
that bred there.

9. • Even though there was a "germ" of truth in miasmatic theory, in that it focused
attention on environmental causes of disease and partly explained social
disparities in health (poor people being more likely to live near foul odors), the
theory began to fall into disfavor as the germ theory gained acceptance.
• Louis Pasteur and others introduced the germ theory in 1878
• In 1890 Robert Koch proposed specific criteria that should be met before
concluding that a disease was caused by a particular bacterium. These became
known as Koch's Postulates, which are as follows:
• The bacteria must be present in every case of the disease.
• The bacteria must be isolated from the host with the disease and grown in pure
culture.
• The specific disease must be reproduced when a pure culture of the bacteria is
inoculated into a healthy susceptible host.
• The bacteria must be recoverable from the experimentally infected host

10. Webb’s Causation
• The germ theory obviously didn't provide insights regarding the
causes of chronic diseases, and over time it became increasingly
apparent that for most diseases there were many contributory
factors.
• Researchers began thinking about complex "webs" of causation.
• The image below summarizes a web of causation for obesity in the
context of a socio-ecologic perspective.
• Note that some factors are more "proximate" or immediate, such as
decreased energy expenditure and increased food intake, while other
factors or perhaps root causes are more distal, such as globalization
of markets, development, and advertising.

12. Cause and effect relationship
• A cause is an event or a condition/characteristic/event that plays an
important role/regular predictable change in occurrence of the
outcome; without which the disease/outcome would not have occurred
in its abscence
Characteristics of a cause factor
1. Must precede the effect
2. Can be either a host or environmental factor (e.g.,
characteristics, conditions, actions of individuals, events, natural, social
or economic phenomena)
3. May be positive (presence of a causative exposure) or negative (lack of
a preventive exposure)

13. Risk Factors versus Causes
• Epidemiologists often use the term "risk factor" to indicate a factor
that is associated with a given outcome.
• However, a risk factor is not necessarily a cause. The term risk factor
includes surrogates for underlying causes.
• For example, consider the following table which summarizes
characteristics associated with a high risk of breast cancer and
characteristics associated with a low risk.

14. Risk factor
High Risk Low risk
Country of Birth
North America, Northern
Europe
Asia, Africa
Socioeconomic status High Low
Marital status Never married Ever married

15. • Each of these factors (place of birth, socioeconomic status, and marital
status) is associated with an increased risk of breast cancer, but none
of these are causes.
• These risk factors are surrogates or markers for underlying causes, e.g.,
populations with a higher prevalence of genetic risk from BRCA1 and
BRCA2 alleles, or lower parity which in turn is a marker for unopposed
estrogen stimulation of breast tissue.)
• Being born in northern Europe per se is not a cause; it is a marker for
populations that may have a greater genetic predisposition to breast
cancer.

16. • It is therefore important to distinguish between risk factors and causes.
• Nevertheless, before one can wrestle with the difficult question of causation,
it is first necessary to establish that a valid association exists.
1. The evidence must be examined to determine that there is a valid
association between an exposure and an outcome.
This is achieved by conducting epidemiologic studies and critically reviewing
the available studies to determine whether random error or bias or
confounding might explain the apparent association.
2. If it is determined that there is a valid association, then one must wrestle
with the question of whether the association was causal.
Not all associations are causal.
There are no standardized rules for determining whether a relationship is
causal.

17. Hill’s criteria for casuality
• In 1939, a German study reported an association between smoking and
lung cancer.
• A succession of studies that sought to examine the cause of the epidemic
of lung cancer
• Richard Doll and Austin Bradford Hill (shown on the right) conducted
landmark epidemiologic studies that were important in establishing the
strong association between smoking and lung cancer.
• The first was a case-control study conducted in London area hospitals.
The cases were patients with lung cancer, and the controls were age and
gender matched patients at the same hospital who had diseases other
than cancer

18. • After their case-control study, Doll and Hill launched a prospective
cohort study among male physicians in the UK, looking at cause of
death as the primary endpoint.
• The initial findings were published in 1954, with a follow up report in
1958.
• These studies demonstrated an even stronger association between
smoking and lung cancer mortality and also showed that smoking was
also significantly associated with other cancers and with a variety of
other non-cancerous causes of death including emphysema, chronic
bronchitis, TB, atherosclerotic heart disease, stroke, hypertension, and
aneurysms

19. • Despite the strong associations that they found, there was controversy about
whether the association was causal.
• Out of this debate came the notion that causality could not be proven by
formulaic consideration of observations; instead, a conclusion of causality was a
judgment based on a body of evidence
• In 1965 Hill and others proposed certain aspects of evidence that should be
considered when trying to draw conclusions about causality as shown below.
1. Strength of the association
2. Consistency
3. Specificity
4. Temporality
5. Biological gradient
6. Plausibility/Coherence
7. Experiment
8. Analogy

20. 1. Strength of association
• The stronger the association, the more likely it is that the relation is
causal.
2. Temporal relationship
• Exposure always precedes the outcome.
3. Consistency
• The association is consistent when results are replicated with different
people under different circumstances and with different measurement
instruments.
4. Theoretical plausibility
• It is easier to accept an association as causal when there is a rational
and theoretical basis for such a conclusion.

21. 5. Coherence
• The association should be compatible with existing theory, hypotheses, and
knowledge.
6. Specificity
• In the ideal situation, the effect has only one cause.
7. Dose response relationship
• An increasing amount of exposure increases the risk.
8. Experimental evidence
• Any related research that is based on experiments will make a causal
inference more plausible.
9. Analogy
• Sometimes a commonly accepted phenomenon in one area can be applied to
another area.

22. Rothman’s casual pies
• Necessary cause – must always precede the effect. Doesn’t need to
be the sole result of the cause e.g tubercle bacillus is necessary for
tuberculosis
Has the disease Doesn’t have the disease
Exposed Yes Yes
Non exposed No Yes

23. • Sufficient cause – inevitably/single handedly initiates or produces an
effect, included component causes. Sufficient exposures are very rare in
medicine in that it is exceptional that one exposure is by itself enough
to cause a disease e.g measles virus gives you clinical measles.
• Component causes – together they constitute a sufficient cause for the
outcome in question.
• Multiple causes result in what is known as the ‘web of causation’
• A component cause can be a component of more than one sufficient
cause. If and only if all the component causes that make up a causal pie
of some sufficient cause are present does the outcome occur
Diseased Not diseased
Exposed Yes No
Non exposed No No

24. The causal pie model.

25. • Component causes A–E add up to sufficient causes I–III.
• Every sufficient cause consists of different component causes. If and
only if all the component causes that constitute the causal pie of a
sufficient cause are present, does the sufficient cause exist and does the
outcome occur.
• Hence, the effect of a component cause depends on the presence of
its complementary component causes, that is, its complementary set. I,
II, and III can be sufficient causes for the same outcome, or for different
outcomes, in which case the outcomes are correlated through the
component causes.

26. • If all component causes of some causal pie are present, then the
sufficient cause is present and the outcome occurs

28. • Natural selection in the causal pie model.
• Selection depends on the effect of presence versus absence of the
component cause.
• (A) If one of the component causes is absent, the other component causes
have no effect (barring effects through other causal pies) and are not
subject to natural selection, as indicated by the dots being black.
• (B) If all the component causes are present, the outcome occurs, and all
the component causes are subject to the force of selection on the
outcome, because without any one of them, the outcome would not occur.
If the component causes are A–D and the force of selection on the
outcome is SΩ, then the force of selection on every component cause, SX,
equals SΩ: SA = SB = SC = SD = SΩ.

29. Necessary cause but insufficient cause
• For a disease with different causation mechanisms, a necessary but not
sufficient cause must be present in all the mechanisms for the outcome to
occur.
• It alone can not cause the outcome without other component causes

30. Necessary and sufficient causes
• Cause that single handedly can cause the outcome of interest
• E.g HIV was once known as the necessary and sufficient cause for
AIDS
• However, with the evolution of ARVs, one may be infected with HIV
but doesn’t get AIDS.
In such a case, we say that HIV is necessary for AIDS to happen but not
sufficient enough to cause AIDS

31. Necessary and sufficient causes
Diseased Not diseased
Exposed All None
Non exposed None All

32. Sufficient but not necessary cause
• A cause is sufficient on its own to cause an outcome but it doesn’t
have to be present all the time for the outcome to occur.
• There are other necessary causes for the outcome to occur
• This means that the disease has different mechanisms of occurring.

33. END