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Extending the sufficient component cause model to describe the Stable Unit Treatment Value Assumption (SUTVA).

Schwartz S, Gatto NM, Campbell UB - Epidemiol Perspect Innov (2012)

Bottom Line: The exchangeability or no confounding assumption is well known and well understood as central to this task.More recently the epidemiologic literature has described additional assumptions related to the stability of causal effects.Approaching SUTVA from an SCC model helps clarify what SUTVA is and reinforces the connections between interaction and SUTVA.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Epidemiology, Mailman School of Public Health, Columbia University, 722 West 168 Street, NY, New York 10032, USA. sbs5@columbia.edu.

ABSTRACT
Causal inference requires an understanding of the conditions under which association equals causation. The exchangeability or no confounding assumption is well known and well understood as central to this task. More recently the epidemiologic literature has described additional assumptions related to the stability of causal effects. In this paper we extend the Sufficient Component Cause Model to represent one expression of this stability assumption--the Stable Unit Treatment Value Assumption. Approaching SUTVA from an SCC model helps clarify what SUTVA is and reinforces the connections between interaction and SUTVA.

No MeSH data available.


Simple SCC model and corresponding response types.
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Figure 1: Simple SCC model and corresponding response types.

Mentions: Figure 1 depicts a minimal Sufficient Component Cause model that describes the effects of an exposure on an outcome. We assume that there are no SUTVA violations in this example. The exposure is precisely defined and therefore we can easily imagine an intervention that would remove the exposure from the population. In the first sufficient cause, this precisely defined exposure works with its "causal partners" (denoted by U) to cause disease. By "causal partners", we mean the other component causes that activate or allow the exposure to cause the disease. A second sufficient cause indicates that the exposure can be preventive (denoted by Ê, the absence of exposure) in the context of other causal partners (W). Individuals can also get the disease from mechanisms that do not include the exposure under study; these other unspecified mechanisms are denoted by X.


Extending the sufficient component cause model to describe the Stable Unit Treatment Value Assumption (SUTVA).

Schwartz S, Gatto NM, Campbell UB - Epidemiol Perspect Innov (2012)

Simple SCC model and corresponding response types.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3351730&req=5

Figure 1: Simple SCC model and corresponding response types.
Mentions: Figure 1 depicts a minimal Sufficient Component Cause model that describes the effects of an exposure on an outcome. We assume that there are no SUTVA violations in this example. The exposure is precisely defined and therefore we can easily imagine an intervention that would remove the exposure from the population. In the first sufficient cause, this precisely defined exposure works with its "causal partners" (denoted by U) to cause disease. By "causal partners", we mean the other component causes that activate or allow the exposure to cause the disease. A second sufficient cause indicates that the exposure can be preventive (denoted by Ê, the absence of exposure) in the context of other causal partners (W). Individuals can also get the disease from mechanisms that do not include the exposure under study; these other unspecified mechanisms are denoted by X.

Bottom Line: The exchangeability or no confounding assumption is well known and well understood as central to this task.More recently the epidemiologic literature has described additional assumptions related to the stability of causal effects.Approaching SUTVA from an SCC model helps clarify what SUTVA is and reinforces the connections between interaction and SUTVA.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Epidemiology, Mailman School of Public Health, Columbia University, 722 West 168 Street, NY, New York 10032, USA. sbs5@columbia.edu.

ABSTRACT
Causal inference requires an understanding of the conditions under which association equals causation. The exchangeability or no confounding assumption is well known and well understood as central to this task. More recently the epidemiologic literature has described additional assumptions related to the stability of causal effects. In this paper we extend the Sufficient Component Cause Model to represent one expression of this stability assumption--the Stable Unit Treatment Value Assumption. Approaching SUTVA from an SCC model helps clarify what SUTVA is and reinforces the connections between interaction and SUTVA.

No MeSH data available.