A generalizability score for aggregate causal effect.

Area of Science:

• Epidemiology
• Biostatistics
• Causal Inference

Background:

• Generalizing population-level causal quantities (e.g., average treatment effect) across different populations is common in scientific research.
• Heterogeneity in causal effects and differences in subject characteristics between source and target populations can hinder reliable generalization.
• Existing methods like reweighting or regression may suffer from high variance when covariate distribution overlap is limited.

Purpose of the Study:

• To propose a novel generalizability score to address challenges in causal effect generalization.
• To provide a metric for selecting suitable target subpopulations for reliable generalization.
• To develop a simplified score variant that mitigates bias by excluding outcome information.

Main Methods:

• Development of a generalizability score to quantify the suitability of target subpopulations.
• Introduction of a simplified score version that does not require outcome data, preventing potential biases.
• Validation through simulation studies and real-world data analysis.

Main Results:

• The proposed generalizability score effectively serves as a yardstick for selecting target subpopulations.
• The simplified score variant successfully avoids biases associated with outcome information.
• Both simulation and real data analyses confirmed the score's utility and convincing results.

Conclusions:

• The generalizability score offers a robust method for improving the reliability of causal effect generalization.
• The score facilitates informed selection of target subpopulations, particularly in cases of limited covariate overlap.
• The outcome-independent version enhances the score's applicability and reduces the risk of deliberate or inadvertent bias.