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Causal Inference
Dr. Amna Rehana Siddiqui
Department of Family and
Community Medicine
Objectives:

Explain basic models of disease causation.

To understand concepts related to scientific
inference for cause effect relation

To understand the applicability of causal
criteria as applied to epidemiological
studies
Approach to etiology

To see whether a certain substance is an
agent / microorganism; a controlled
laboratory experiment can be done by
 Exposing
animals to organism
 Setting the exposure dose
 Monitoring environmental conditions
 Selecting genetic factors
 Minimum loss to follow up
 Species differ in response
Observations in Human populations




Cannot randomize human beings for harmful
substances
Depend on nonrandomized observations
Important populations – occupational cohorts
Natural experiments
 Residents
of Hiroshima and Nagasaki
 Residents of Bhopal
Stages of disease and
Levels of prevention

Susceptibility

Primary prevention

Secondary prevention

Pre-symptomatic

Clinical

Tertiary prevention

Disability or Recovery

Tertiary prevention
(Screening)
Development of Disease
Combination of events



A harmful agent
A susceptible host
An appropriate environment
General Models of Causation

In epidemiology, there are several models of disease
causation that help understand disease process.

The most widely applied models are:

The epidemiological triad (triangle),

the wheel, and

the web. And

The sufficient cause and component causes models (Rothman’s
component causes model)
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The Epidemiologic Triad
HOST
AGENT
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ENVIRONMENT
Agent factors
•Infectious agents: agent might be microorganism—virus,
bacterium, parasite, or other microbes. e.g. polio, measles,
malaria, tuberculosis Generally, these agents must be present
for disease to occur.
•Nutritive: excesses or deficiencies (Cholesterol, vitamins,
proteins)
•Chemical agents: (carbon monoxide, drugs, medications)
•Physical agents (Ionizing radiation,…
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Host factors
•Host factors are intrinsic factors that influence an individual’s
exposure, susceptibility, or response to a causative agent.
•Host factors that affect a person’s risk of exposure to an agent:
•e.g. Age, race, sex, socioeconomic status, and behaviors
(smoking, drug abuse, lifestyle, sexual practices and eating
habits)
•Host factors which affect susceptibility &response to an agent:
•Age, genetic composition, nutritional and immunologic status,
anatomic structure, presence of disease or medications, and
psychological makeup.
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Environmental factors
Environmental factors are extrinsic factors which affect the agent
and the opportunity for exposure.
Environmental factors include:
 physical
factors such as geology, climate,..
 biologic
factors such as insects that transmit an agent; and
 socioeconomic
factors such as crowding, sanitation, and the
availability of health services.
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Malaria
Agent
Vector
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Host
Environment
The epidemiologic triad Model
Host:
Intrinsic factors, genetic, physiologic factors,
psychological factors, immunity
Health
or
Illness
?
Agent:
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Amount, infectivity, pathogenicity,
virulence, chemical composition,
cell reproduction
Environment:
Physical, biological, social
Web of Causation
 There
is no single cause
 Causes
of disease are interacting
 Illustrates
the interconnectedness of
possible causes
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The Web of causation
Developed to de-emphasis agent
 Chain of causation
 Complexity of origin is web
 Multiple factors promote or inhibit
 Emphasizes multiple interactions between
host and environment

Web of Causation
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Web of Causation - CHD
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RS Bhopal
Example of a Web of Causation
Overcrowding
Malnutrition
Exposure to
Mycobacterium
Susceptible Host
Infection
Tuberculosis
Tissue Invasion
and Reaction
Vaccination
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Genetic
The Wheel of Causation

The Wheel of Causation de-emphasizes the
agent as the sole cause of disease,

It emphasizes the interplay of physical, biological
and social environments. It also brings genetics
into the mix.
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The Wheel of Causation
Social
Environment
Biological
Environment
Host
(human)
Genetic Core
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Physical
Environment
Association Vs. Causation

Association refers to the statistical dependence
between two variables

The presence of an association…in no way implies
that the observed relationship is one of cause and
effect
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Types of causes

Sufficient causes:
a
set of conditions without any one of which the
disease would not have occurred
 not usually a single factor, often several

Necessary cause:
 must
be present for disease to occur, disease never
develops in the absence of that factor.
 a component cause that is a member of every
sufficient cause
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The sufficient cause and component causes model
Rothman’s component causes model
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Necessary and sufficient causes

A necessary cause is a causal factor whose presence is
required for the occurrence of the effect. If disease does
not develop without the factor being present, then we term
the causative factor “necessary”.

Sufficient cause is a “minimum set of conditions, factors or
events needed to produce a given outcome.

The factors or conditions that form a sufficient cause are
called component causes.
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Example

The tubercle bacillus is required to cause
tuberculosis but, alone, does not always
cause it,

so tubercle bacillus is a necessary, not a
sufficient, cause.
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Rothman’s Component Causes and
Causal Pies Model

Rothman's model has emphasised that the causes of disease
comprise a collection of factors.

These factors represent pieces of a pie, the whole pie
(combinations of factors) are the sufficient causes for a
disease.

It shows that a disease may have more that one sufficient
cause, with each sufficient cause being composed of several
factors.
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Rothman’s
Component Causes and Causal Pies

The factors represented by the pieces of the pie in this model
are called component causes.

Each single component cause is rarely a sufficient cause by
itself, But may be necessary cause.

Control of the disease could be achieved by removing one of
the components in each "pie" and if there were a factor
common to all "pies“ (necessary cause) the disease would be
eliminated by removing that alone.
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Exercise

Some of the risk factors for heart disease are smoking,
hypertension, obesity, diabetes, high cholesterol, inactivity,
stress, and type A personality.
- Are these risk factors necessary causes, sufficient causes,
or component causes?
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Causal pies representing all sufficient causes of a
particular disease
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Types of Associations
Real: probability depends upon the
occurrence of one or more other events,
characteristics, or other variables
 Spurious: Non causal associations depend
on bias, chance, failure to control for
extraneous variables (confounding)

Percentage of pregnancies (n=50,267) with infant
weighing < 2500 g by maternal cigarette smoking
category (peri-natal mort study Comm Vol 1, 1967
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12
12
% less than
2500 g
10
7.7
8
6
4.7
4
2
0
Non smoker
< 1 pack
>=1 pack
Percentage of LBW infants by smoking
status of their mothers (Yerushalmay J, Am J Obs & Gynecol)
9.5
10
% of
LBW 8
infants
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8.9
6
5.3
4
2
0
Non Smoker
Non Smoker
All pregnancies Future
Smoker
Smoker
All Preg
Smoker
Future
Ex smoker
“Is there an association between an exposure and
a disease?”
IF SO….
 Is the association likely to be due to chance?
 Is the association likely to be due to bias?
 Is the association likely to be due to
confounding?
 Is the association real/causal?
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Establishing the cause of disease
Association?
absent
present
Chance?
present
absent
Bias ?
likely
Confounding?
likely
absent
absent
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Causal?
Association Vs. Causation

Association refers to the statistical dependence
between two variables

The presence of an association…in no way implies
that the observed relationship is one of cause and
effect
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Epidemiological criteria (guidelines) for
causality

An association rarely reflects a causal
relationship but it may.

Criteria for causality provide a way of
reaching judgements on the likelihood
of an association being causal.
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Hill’s Criteria for Causal Relation

Strength of association

Consistency of findings
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Specificity of association
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Temporal sequence
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Biological gradient (dose-response)
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Biological plausibility
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Coherence with established facts
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Experimental evidence
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Strength of association association
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Does exposure to the cause change disease
incidence?
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The strength of the association is measured by the
relative risk.
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The stronger the association, the higher the
likelihood of a causal relationship.

Strong associations are less likely to be caused by
chance or bias
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Consistency of findings

Consistency refers to the repeated observation of an
association in different populations under different
circumstances.

Causality is more likely when the association is repeated by
other investigations conducted by different persons in different
places, circumstances and time-frames, and using different
research designs.
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Specificity of association

It means that an exposure leads to a single or characteristic
effect, or affects people with a specific susceptibility
 easier
to support causation when associations are
specific, but
 this


may not always be the case
as many exposures cause multiple diseases
This is more feasible in infectious diseases than in noninfectious diseases, which can result from different risk
agents.
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Temporal sequence
(temporality)
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Did the cause precede the effect?

Temporality refers to the necessity that the cause must
precede the disease in time.

This is the only absolutely essential criterion.

It is easier to establish temporality in experimental and
cohort studies than in case-control and cross-sectional
studies.
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Biological gradient

Does the disease incidence vary with the level of exposure?
(dose-response relationship)
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Changes in exposure are related to a trend in relative risk

A dose-response relationship (if present) can increase the
likelihood of a causal association.
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Biological gradient
(Dose Response)
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Age standardized death rates due to bronchogenic
carcinoma by current amount of smoking

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Dose-response relationship
Biological plausibility

Is there a logical mechanism by which the
supposed cause can induce the effect?

Findings should not disagree with established
understanding of biological processes.
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Coherence

Coherence implies that a cause-and-effect
interpretation for an association

does not conflict with what is known of the
natural history and biology of the disease
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Experimental evidence

It refers to evidence from laboratory
experiments on animal or to evidence from
human experiments

Causal understanding can be greatly advanced
by laboratory and experimental observations.
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Judging the causal basis of the association

No single study is sufficient for causal inference

It is always necessary to consider multiple alternate
explanations before making conclusions about the
causal relationship between any two items under
investigation.

Causal inference is not a simple process

consider weight of evidence

requires judgment and interpretation
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Figure 5.12 The scales of causal judgement
Weigh up weaknesses in data
and alternative explanations
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Weigh up quality of science
and results of applying causal
frameworks
Pyramid of Associations
Causal
Non-causal
Confounded
Spurious / artefact
Chance
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RS Bhopal
Evaluating Evidence of Causal
relationship
Major Criteria
a. Temporal relationship
b. Biologic plausibility
c. Consistency of Results
d. Alternative explanations
Other criteria
a. Strength of association
b. Dose-response relationship
c. Cessation of effects

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