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

Area of Science:

• Epidemiology
• Causal Inference
• Statistical Modeling

Background:

• Causal inference hinges on assumptions linking association to causation.
• The exchangeability (no confounding) assumption is fundamental.
• Epidemiologic literature increasingly emphasizes causal effect stability.

Purpose of the Study:

• To extend the Sufficient Component Cause (SCC) model.
• To represent the Stable Unit Treatment Value Assumption (SUTVA) within the SCC framework.
• To clarify the nature of SUTVA and its relationship with interaction.

Main Methods:

• Utilizing the Sufficient Component Cause (SCC) model.
• Formally integrating the Stable Unit Treatment Value Assumption (SUTVA) into the SCC framework.
• Analyzing the connections between interaction and SUTVA.

Main Results:

• The SCC model provides a framework for understanding SUTVA.
• This approach clarifies the meaning and implications of SUTVA.
• The study reinforces the link between interaction and SUTVA.

Conclusions:

• Representing SUTVA via the SCC model enhances understanding of causal effect stability.
• The SCC model offers a structured way to conceptualize and apply SUTVA.
• This work deepens the understanding of assumptions crucial for valid causal inference.