Equivalence model: A new graphical model for causal inference.

Area of Science:

• Epidemiology
• Causal Inference
• Biostatistics

Background:

• Existing causal models do not fully explain disease occurrence disparities.
• A gap exists in understanding why risk and protective factors lead to varied individual health outcomes.

Purpose of the Study:

• To introduce the equivalence model for understanding disease causality.
• To provide a graphical framework for assessing the interplay of risk and protective factors at the individual level.

Main Methods:

• Proposed the equivalence model, defining risk factors as disease facilitators and protective factors as inhibitors.
• Described the model's graphical representation of factor interactions.
• Established conditions for disease occurrence based on the balance of risk and protective factor units.

Main Results:

• The equivalence model demonstrates that disease occurs when risk factor units exceed protective factor units.
• A balance or deficit of risk factors relative to protective factors prevents disease.
• The model explains differential disease susceptibility among individuals with varying risk exposures.

Conclusions:

• The equivalence model offers a novel approach to causal inference in complex health scenarios.
• It provides a clear explanation for paradoxical disease development in high- and low-risk populations.
• This model enhances understanding of individual-level disease etiology.