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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.


SUTVA violations from interference between units.
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Figure 3: SUTVA violations from interference between units.

Mentions: In this violation of SUTVA, although a single exposure was randomly assigned, the exposure essentially has two versions: one version when the individual receives the exposure in the presence of exposed influential others and another version when the individual receives the exposure in the presence of unexposed influential others. A Sufficient Component Cause model underlying this scenario is shown at the top of Figure 3. An individual's own exposure condition is indicated as E if exposed and Ê if not exposed. The exposure condition of influential others is indicated as I if the influential others are exposed and as Î if the influential others are unexposed.


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)

SUTVA violations from interference between units.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: SUTVA violations from interference between units.
Mentions: In this violation of SUTVA, although a single exposure was randomly assigned, the exposure essentially has two versions: one version when the individual receives the exposure in the presence of exposed influential others and another version when the individual receives the exposure in the presence of unexposed influential others. A Sufficient Component Cause model underlying this scenario is shown at the top of Figure 3. An individual's own exposure condition is indicated as E if exposed and Ê if not exposed. The exposure condition of influential others is indicated as I if the influential others are exposed and as Î if the influential others are unexposed.

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.