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Related Experiment Video

Updated: May 24, 2026

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

Sensitivity analysis for unmeasured confounding in principal stratification settings with binary variables.

Scott Schwartz¹, Fan Li, Jerome P Reiter

¹Department of Statistics, Texas A&M University, College Station, TX 77843-3143, USA. scott@stat.tamu.edu

Statistics in Medicine

|February 25, 2012

Summary

Principal stratification (PS) analysis can yield biased causal estimates due to unmeasured confounding. This study introduces methods to assess sensitivity to confounding in PS, ensuring more reliable causal inference.

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Last Updated: May 24, 2026

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

Area of Science:

Causal Inference
Biostatistics
Epidemiology

Background:

Principal stratification (PS) is used for intermediate variables in causal inference.
Unmeasured confounding in treatment arms can bias PS causal estimands, especially in observational studies.

Purpose of the Study:

Identify confounding pathways in PS inference.
Develop methods to assess sensitivity of causal effects to unmeasured confounding.
Evaluate sensitivity to unknown direct treatment effects.

Main Methods:

Identify and analyze pathways of confounding in PS.
Present model-based sensitivity analysis for binary treatments, intermediate variables, and outcomes.
Demonstrate operational equivalence between direct effects and a confounding pathway.

Main Results:

Established pathways of confounding impacting PS inference.
Developed and illustrated sensitivity analysis methods.
Showed direct effects are equivalent to a specific confounding pathway.

Conclusions:

Unmeasured confounding poses a significant threat to PS validity.
Sensitivity analysis is crucial for robust PS inference.
Methodology applicable to various causal inference scenarios, including observational studies.