Bespoke Instruments: A new tool for addressing unmeasured confounders

Area of Science:

• Epidemiology
• Biostatistics
• Causal Inference

Background:

• Quantifying exposure-disease associations is crucial in research.
• Unmeasured confounders can introduce residual bias.
• Existing methods may not fully address residual confounding.

Purpose of the Study:

• To propose and evaluate a new approach for controlling residual confounding.
• To develop a method for assessing bias due to unmeasured confounders.
• To provide a tool for more accurate causal effect estimation.

Main Methods:

• Utilizing a measured confounder to construct a population-specific instrumental variable.
• Developing conditions for the identification of causal effects.
• Employing simulations and an empirical example for validation.

Main Results:

• The proposed instrumental variable approach effectively accounts for residual bias.
• Demonstrated identification of causal effects under specific conditions.
• Successfully applied the method to analyze mortality in atomic bomb survivors.

Conclusions:

• The bespoke instrumental variable method offers a valuable tool for bias assessment.
• This approach enhances the reliability of causal inference in the presence of unmeasured confounding.
• The method has practical applications in epidemiological studies.