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

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Causality in epidemiology• Goals of epidemiology

– Describing distribution of disease– Predicting disease– Identifying causes of disease

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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)

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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.”

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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)

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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

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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

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Models of causality– Sufficient-component cause model, aka

Rothman’s pies– Counterfactual framework– Graphical models/DAGs– Hill's criteria or “viewpoints”

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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

Sufficient-component cause• Model oriented around mechanisms of

disease causation

A

U

B

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Sufficient-component cause• Example: sufficient cause of impaired brain

function

PKU

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phenylalanine

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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

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

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A

B1B2

C

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

B1U2

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A

B2

C

Sufficient-component cause• Necessary cause = occurs in all sufficient causes;

same “slice” is in every pie– HPV is a necessary cause of cervical cancer– HIV is a necessary cause of AIDS

U1

A

B1U2

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A

B2

C

Sufficient-component cause• Causal complement = for any component cause,

the set of other component causes in the sufficientcause is the causal complement (the rest of the pie)

– A, B2, U2 are the causal complement of C

U1

A

B1U2

16

A

B2

C

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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

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Example• Bicycle

commuting

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

Example• Bicycle falls

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Example• My bicycle fall

– A=road surface, B=traffic, C=speed, D=bicycle characteristics

A

BU

C

D

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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

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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

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Example• Bicycle fall

– Illustration of sufficient-component cause model illuminating strength of associations

Example

U

A

B

C

D

• Study of the effect of traffic on bicycle falls– A=road surface, B=traffic, C=speed, D=bicycle

characteristicsSetting 1: 10% of bicycling

routes have poor quality road surface

Setting 2: 90% of bicycling routes have poor quality road surface

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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)

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Sufficient-component cause• We will return to this model when we

discuss– Strength of associations (on different

scales)– Attributable percentage measures– Effect modification