5.1.1 sufficient component cause model

1. Causality Outline
– Causality
– Models of causality
• Sufficient-component cause model, aka Rothman’s pies
• Counterfactual framework
• Hill’s criteria or “viewpoints”
• Graphical models/DAGs (Presented by Dr. Jade
Benjamin-Chung)
• Causal perspective on effect modification (Presented by
Dr. Jade Benjamin-Chung)
– Summary
1

2. 2
Causality in epidemiology
• Goals of epidemiology
– Describing distribution of disease
– Predicting disease
– Identifying causes of disease

3. 3
Causality
• What is a cause?
• “An antecedent event, condition, or characteristic
that was necessary for the occurrence of the disease
at the moment it occurred, given that other
characteristics are fixed.” (emphasis added)

4. 4
Causality
• “An event, condition, or characteristic that preceded
the disease onset and that, had the event, condition,
or characteristic been different in a specified way,
the disease either would not have occurred at all or
would not have occurred until some later time.”

5. 5
Causality
• “We may define a cause to be an object followed by
another… where, if the first object had not been, the
second never had existed” (Hume 1748)

6. 6
Causality Outline
– Causality
– Models of causality
• Sufficient-component cause model, aka Rothman’s pies
• Counterfactual framework
• Hill’s criteria or “viewpoints”
• Graphical models/DAGs (Will be presented by Dr. Jade Benjamin-Chung)
• Causal perspective on effect modification (Will be presented by Dr.
Jade Benjamin-Chung)
– Summary

7. 7
Models of causality
• Epidemiology uses several “models of causation that
may be useful for gaining insight about
epidemiologic concepts”
– Conceptual model vs statistical model
– Why are there several models and not just one?
• Each causal model has different strengths and helps illuminate
different epidemiologic concepts and accomplish different tasks
relevant to epidemiologic research

8. 8
Models of causality
– Sufficient-component cause model, aka
Rothman’s pies
– Counterfactual framework
– Graphical models/DAGs
– Hill's criteria or “viewpoints”

9. 9
Causality Outline
– Causality
– Models of causality
• Sufficient-component cause model, aka Rothman’s
pies
• Counterfactual framework
• Hill’s criteria or “viewpoints”
• Graphical models/DAGs (Will be presented by Dr. Jade
Benjamin-Chung)
• Causal perspective on effect modification (Will be
presented by Dr. Jade Benjamin-Chung)
– Summary

10. Sufficient-component cause
• Model oriented around mechanisms of
disease causation
A
U
B
10

11. Sufficient-component cause
• Example: sufficient cause of impaired brain
function
PKU
1
1
phenylalanine

12. 1
2
Sufficient-component cause
• Sufficient cause = “a complete causal mechanism, a
minimal set of conditions and events that are
sufficient for the outcome to occur.”
– Can be (and almost always are) more than one for any
outcome
– If none occur, then the outcome will not occur
– Can (and almost always does) include unknown causes

13. Sufficient-component cause
• Example of two sufficient causes of CVD
– A=smoking, B1=genetic profile 1, B2=genetic profile 2,
C=high cholesterol
U1
A
U2
1
3
A
B1
B2
C

14. Sufficient-component cause
• Component cause = one member (one “slice”) of a
set of causes that creates a sufficient cause;
blocking it will result in the outcome not happening
U1
A
B1
U2
1
4
A
B2
C

15. Sufficient-component cause
• Necessary cause = occurs in all sufficient causes;
same “slice” is in everypie
– HPV is a necessary cause of cervical cancer
– HIV is a necessary cause of AIDS
U1
A
B1
U2
1
5
A
B2
C

16. Sufficient-component cause
• Causal complement = for any component cause,
the set of other component causes in the sufficient
cause is the causal complement (the rest of the
pie)
– A, B2, U2 are the causal complement of C
U1
A
B1
U2
1
6
A
B2
C

17. 1
7
Sufficient-component cause
• Unknown component causes (Us)
– The Us are also very important – almost every pie has
some unknown component U
– For almost every outcome there is an entire U pie

18. 18
Example
• Bicycle
commuting
According to the US Census Bureau’s 2008 American
Community Survey (ACS) 0.55% of American workers
use a bicycle as the primary means of getting to work.
This is up 14 percent since 2007, 36 percent from the
first ACS in 2005, and 43 percent since the 2000
Census.

19. Example
• Bicycle falls
1
9

20. Example
• My bicycle fall
– A=road surface, B=traffic, C=speed, D=bicycle
characteristics
A
B
U
C
D
2
9

21. 2
1
S-c cause contributions
• Depicts multifactorial causality in disease
• Clearly illustrates “necessary” and “sufficient”
causes
• Illuminates meaning of strength of associations,
attributable percentages, and effect modification
(will return to this throughout the course)
– Note: effect modification exists when the effect of one
exposure is different depending on the value of another
exposure

22. 2
2
S-c cause limitations
• More useful in concept than in application to
particular research questions
– What scope of causes get into a pie?
– So many pies for most outcomes
• Not helpful for depicting sequential mechanisms or
direct and indirect effects
• Specifies details that go beyond the scope of
epidemiologic data

23. 2
3
Example
• Bicycle fall
– Illustration of sufficient-component cause model
illuminating strength of associations

24. Example
U
A
B
C
D
• Study of the effect of traffic on bicycle falls
– A=road surface, B=traffic, C=speed, D=bicycle
characteristics
Setting 1: 10% of bicycling
routes have poor quality road
surface
Setting 2: 90% of bicycling
routes have poor quality road
surface
2
4

25. 2
5
Example
• Will the strength of the association between traffic
and falls appear the same in settings 1 and 2?
– Assuming frequency of other component causes of this
sufficient cause and other distinct sufficient causes are
the same between settings
• Strength of association depends on how common
the rest of the pie is in the population you study
(i.e., strength of association depends on the
prevalence of the causal complement)

26. 2
6
Sufficient-component cause
• We will return to this model when we
discuss
– Strength of associations (on different scales)
– Attributable percentage measures
– Effect modification