Sensitivity to Unobserved Confounding in Studies with Factor-structured Outcomes.

Area of Science:

• Epidemiology
• Biostatistics
• Causal Inference

Background:

• Assessing unobserved confounding is crucial for valid causal inference.
• Analyzing multiple outcomes individually may not fully leverage available information.
• Sensitivity analysis is vital for understanding potential biases in observational studies.

Purpose of the Study:

• To propose a novel approach for sensitivity analysis in studies with multiple outcomes.
• To demonstrate how multi-outcome data can strengthen causal conclusions.
• To develop methods for quantifying the robustness of causal effect estimates.

Main Methods:

• Utilizing a shared confounding assumption for multi-outcome data.
• Employing factor models to bound causal effects using a single sensitivity parameter.
• Characterizing the reduction in causal ignorance regions with additional prior assumptions.
• Illustrating the workflow with simulated and real-world data (NHANES).

Main Results:

• The proposed method simplifies and sharpens sensitivity analyses by exploiting residual outcome dependence.
• Causal ignorance regions shrink under specific prior assumptions, such as the presence of null control outcomes.
• The approach provides a quantifiable measure of robustness for causal effect estimates.

Conclusions:

• Leveraging multi-outcome data and shared confounding assumptions enhances causal inference beyond single-outcome analyses.
• The developed sensitivity analysis framework offers a practical tool for assessing the reliability of causal findings.
• This work provides new methods for quantifying the robustness of estimates in the presence of unobserved confounding.