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Open Access Analytic Perspective

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

Sharon Schwartz1*, Nicolle M Gatto23 and Ulka B Campbell23

Author Affiliations

1 Department of Epidemiology, Mailman School of Public Health, Columbia University, 722 West 168 Street, NY, New York 10032, USA

2 Department of Epidemiology, Columbia University Mailman School of Public Health, NY, New York, USA

3 Epidemiology, Worldwide Safety Strategy, Pfizer Inc, NY, New York, USA

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Epidemiologic Perspectives & Innovations 2012, 9:3  doi:10.1186/1742-5573-9-3

Published: 3 April 2012


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.

Causal inference; Consistency assumption; Counterfactuals; Stability assumption; SUTVA