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Deterministic Approach

to CausalityDr. Shahram

Yazdani

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Definition

• Causality refers to the way of knowing that one thing causes another.

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Sufficient Cause Cluster: a Deterministic Approach

• A sufficient cause cluster which means a complete causal mechanism, can be defined as a set of minimal conditions and events that inevitably produce effect.

• Minimal implies that all of the conditions and events are necessary.

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Necessary Cause

• A necessary cause can be defined as a conditions and events that without which the effect does not occur.

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An effect with one sufficient cause cluster with two component cause

• A is a necessary cause• B is a necessary cause• A and B are a sufficient cause

cluster

A

B

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An effect with three sufficient cause cluster

• U is a necessary cause for the effect

Three sufficient cause cluster of a disease

UBA

UEA

UEB

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Sufficient cause cluster

• When causal components remain unknown, one may be inclined to assign an equal risk to all individuals whose status for some components is known and identical.

• Thus, men who are heavy smokers are said to have approximately a 10% lifetime risk of developing lung cancer.

• Some interpret this statement to mean that all men would be subject to a 10% probability of lung cancer if they were to become heavy smokers, as if the outcome, aside from smoking, were a matter of chance

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Sufficient cause cluster

• We view the assignment of equal risks as reflecting nothing more than assigning to everyone within a specific category

• In the classic view, these risks are either one or zero, according to whether the individual will or will not get lung cancer.

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Strength of Effect:

Three sufficient causes of a disease

UBA

UEA

UEB

Exposure* Outcome

Exposure Frequency

A B E Population 1

Population 2

1 1 1 1 100 900

1 1 0 1 100 900

1 0 1 1 900 100

1 0 0 0 900 100

0 1 1 1 900 100

0 1 0 0 900 100

0 0 1 0 100 900

0 0 0 0 100 900*Suppose that U is always present

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Strength of Effect: case to population ratio in population 1

Exposure* Outcome

Exposure Frequency

A B E Population 1

Population 2

1 1 1 1 100 900

1 1 0 1 100 900

1 0 1 1 900 100

1 0 0 0 900 100

0 1 1 1 900 100

0 1 0 0 900 100

0 0 1 0 100 900

0 0 0 0 100 900*Suppose that U is always present

2000 case from 4000 population

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Strength of Effect: case to population ratio in population 2

Exposure* Outcome

Exposure Frequency

A B E Population 1

Population 2

1 1 1 1 100 900

1 1 0 1 100 900

1 0 1 1 900 100

1 0 0 0 900 100

0 1 1 1 900 100

0 1 0 0 900 100

0 0 1 0 100 900

0 0 0 0 100 900*Suppose that U is always present

2000 case from 4000 population

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Strength of Effect:

Exposure* Outcome

Exposure Frequency

A B E Population 1

Population 2

1 1 1 1 100 900

1 1 0 1 100 900

1 0 1 1 900 100

1 0 0 0 900 100

0 1 1 1 900 100

0 1 0 0 900 100

0 0 1 0 100 900

0 0 0 0 100 900*Suppose that U is always present

Incidence of A in population 1

=50%

Incidence of A in population 2

=50%

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Strength of Effect:

Exposure* Outcome

Exposure Frequency

A B E Population 1

Population 2

1 1 1 1 100 900

1 1 0 1 100 900

1 0 1 1 900 100

1 0 0 0 900 100

0 1 1 1 900 100

0 1 0 0 900 100

0 0 1 0 100 900

0 0 0 0 100 900*Suppose that U is always present

Incidence of B in population 2

=50%

Incidence of B in population 1

=50%

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Strength of Effect:

Exposure* Outcome

Exposure Frequency

A B E Population 1

Population 2

1 1 1 1 100 900

1 1 0 1 100 900

1 0 1 1 900 100

1 0 0 0 900 100

0 1 1 1 900 100

0 1 0 0 900 100

0 0 1 0 100 900

0 0 0 0 100 900*Suppose that U is always present

Incidence of E in population 1

=50%

Incidence of E in population 2

=50%

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But we are not aware from all cause clusters and all components of each cause cluster

UBA

UEA

UEB

Exposure* Outcome

Exposure Frequency

A B E Population 1

Population 2

1 1 1 1 100 900

1 1 0 1 100 900

1 0 1 1 900 100

1 0 0 0 900 100

0 1 1 1 900 100

0 1 0 0 900 100

0 0 1 0 100 900

0 0 0 0 100 900*Suppose that U is always present

Suppose that we are not

aware from the cause

component A

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Exposure* Outcome

Exposure Frequency

A B E Population 1

Population 2

1 1 1 1 100 900

1 1 0 1 100 900

1 0 1 1 900 100

1 0 0 0 900 100

0 1 1 1 900 100

0 1 0 0 900 100

0 0 1 0 100 900

0 0 0 0 100 900*Suppose that U is always present

Suppose that we are not

aware from the cause

component A

100% of people in both groups with B and E have disease0% of people in both groups without B and E are healthy10% of people in group 1 and 90% of people in group 2 with B but without E have disease

90% of people in group 1 and 10% of people in group 2 with E but without B have disease

What do you infer about strength of association of B and E with disease if you do the study in population 1

What do you infer about strength of association of B and E with disease if you do the study in population 2

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Strength of EffectsIncidence proportions for combinations of Component causes B and E in population 1, assuming that component cause A is unmeasured

B=1, E=1

B=1, E=0

B=0, E=1

B=0, E=0

Cases 1000 100 900 0

Total 1000 1000 1000 1000

proportion

1.0 0.1 0.9 0E is a much stronger determinant of incidence than BBecause the condition in which E acts as a necessary and sufficient cause –the presence of A or B, but not both- is common (3600 out of 4000 population or 90%)

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Strength of EffectsIncidence proportions for combinations of Component causes B and E in population 2, assuming that component cause A is unmeasured

B=1, E=1

B=1, E=0

B=0, E=1

B=0, E=0

Cases 1000 900 100 0

Total 1000 1000 1000 1000

proportion

1.0 0.9 0.1 0B is a much stronger determinant of incidence than EBecause the condition in which B acts as a necessary and sufficient cause –the presence of A or E, but not both- is common (3600 out of 4000 population or 90%)

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• Although the members of these populations have exactly the same causal mechanisms operating within them, the relative strength of factors E and B are completely different in them

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Causal Complement

• The necessary and sufficient condition for a factor to produce disease is called causal complement of the factor.

• The condition “A or B but not both” is the causal complement of E in previous example.

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Causal Complement and Strength

• The strength of a factor’s effect on a population depends on the relative prevalence of its causal complement; and this strength is independent of the biologic mechanism of the component’s action

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Causal complement for A

UBA

UEA

UEB

Exposure* Outcome

Exposure Frequency

A B E Population 1

Population 2

1 1 1 1 100 900

1 1 0 1 100 900

1 0 1 1 900 100

1 0 0 0 900 100

0 1 1 1 900 100

0 1 0 0 900 100

0 0 1 0 100 900

0 0 0 0 100 900

Select Those Combinations that when turn the value of A from 0 to 1 the outcome value also turns from 0 to 1

Exclude Combinations Containing A

Exclude Combinations Which Leads to Effect

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• In epidemiology, the strength of a factor’s effect is usually measured by the change in disease frequency produced by introducing the factor into population.

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• for any cause component, observed strength of effect is an epidemiologic concept and not a biologic one.

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• Despite U is a necessary cause component you don’t see any association between it and the effect, and the strength of effect for U in both populations is Zero

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Interaction among causes

• Biologic interaction can be defined as the participation of two component causes in the same sufficient cause cluster.

• Such interaction is also known as causal co-action or joint action.

• The joint action of the two component causes does not have to be simultaneous action.

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Interaction among causes: example• Suppose a traumatic injury to the

head leads to a permanent disturbance in equilibrium. Many years later, the faulty equilibrium may lead to a fall while walking on an icy path, causing a broken hip.

• These two component causes have interacted with one another, although their time of action is many years apart.

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Observable interaction among causes• The degree of observable

interaction between two specific component causes depends on how many different sufficient cause clusters produce disease and the proportion of of cases that occurs through sufficient causes in which the two component causes both play some role.

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Interaction among causes

• Suppose that G does not exist in a population. Consequently, no disease would occur from sufficient cause cluster 2, and factors B and F would act only through the distinct mechanisms represented by sufficient cause clusters 1 and 3. Thus, B and F would be biologically independent.

AB

C DE

AB

F GH

AC

F IJ

1 2 3

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Interaction among causes

• Now suppose G is present; then B and F would interact biologically through cause cluster 2.

• Furthermore, if C is completely absent, then cases will occur only when factors B and F act together in the mechanism represented by sufficient cause cluster 2.

• Thus, the extent or apparent strength of biologic interaction between two factors is dependent on the prevalence of other factors.

AB

C DE

AB

F GH

AC

F IJ

1 2 3

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Proportion of Disease due to specific causes

• Assuming that the three sufficient causes in the diagram are the only ones operating,

• what fraction of disease is caused by U ? All of it

• This is not to say that all disease is due to U alone or that a fraction of disease is due to U alone.

• No cause component acts alone; rather, these factors interact with their complementary factors to produce disease.

UBA

UEA

UEB

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Nine guidelines for judging whether an association is causal Hill (1965)

• Temporal relationship

• Strength of association

• Dose response relationship

• Replication of the findings

• Biologic plausibility

• Consideration of alternate explanations

• Cessation of exposure

• Specificity of the association

• Consistency with other knowledge

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Deterministic Approach to CausalityA Researcher’s Point of View

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Observation about disease P

• Prevalence of disease P is 18%• 61% of patients with disease P have factor A• 21% of people with factor A have disease P• Suppose that the association of A and P is

causal• Draw the causal map of disease P?

A

• What kind of research do you propose for clarification of etiology of disease P

?A

?

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People

With A

Without A

With disease P

Without disease P

With disease P

Without disease P

Difference

Difference

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Observation about disease P (2)• 100% of A+/P+ people have B• 19% of A+/P- people have B• 57% of A+/B+ people have P• 100% of A-/P+ people have B• 63% of A-/P- people have B• Suppose that the association of B and P is

causal• Draw the causal map of disease P?

• What kind of research do you propose for clarification of etiology of disease P

A

B B

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People

A+/B+

A-/B+

With disease P

Without disease P

With disease P

Without disease P

Difference

Difference

B-

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Observation about disease P• 63% of A+/B+/P+ people have C• 0% of A+/B+/P- people have C• 100% of A+/B+/C+ people have P• 100% of A-/B+/P+ people have D• 0% of A-/B+/P- people have D• Suppose that the association of C and D with P is

causal• Draw the causal map of disease P?

• What is your inference if you know that all of the 37% of A+/B+/P+ people without C are D+?

A

B C

D

B

A

B

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A

B C

D

B

39%

89%

A B C D P prevalence

1 1 1 1 1 5

1 1 1 0 1 2

1 1 0 1 1 4

1 1 0 0 0 8

1 0 1 1 0 16

1 0 1 0 0 7

1 0 0 1 0 6

1 0 0 0 0 4

0 1 1 1 1 2

0 1 1 0 0 25

0 1 0 1 1 5

0 1 0 0 0 1

0 0 1 1 0 3

0 0 1 0 0 6

0 0 0 1 0 1

0 0 0 0 0 5

Prevalence of A: 52%

Prevalence of B: 52%

Prevalence of C: 66%

Prevalence of D: 42%

What is the perceived association between A and BWhat is the perceived association between A and B

If factor C is present in all people

73%

61%

100%

0%

What is the perceived association between A and BIf factor D is absent in all people

57% of A+/B+ people have P

100% of A+/B+ people have P57% of A+/B+ people have P

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Thank You !Any Question ?