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Non-random selection with and without bias due to selecting on an exposure
Chanelle J Howe1,2,3, Han-Chih T Hsieh1,2, Jason R Gantenberg1,4,5
1Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA.
Epidemiology (Cambridge, Mass.)
|July 6, 2026
Summary
Non-random selection does not always cause bias in causal effect estimation. Understanding when selection bias is absent can improve study design and analysis, leading to more accurate results.
Area of Science:
- Epidemiology
- Causal Inference
- Biostatistics
Background:
- Non-random selection is a significant threat to estimating causal effects accurately.
- Existing literature often emphasizes when bias occurs, neglecting scenarios where it does not.
Purpose of the Study:
- To explore and illustrate situations where non-random selection does not introduce bias.
- To deepen the understanding of selection bias and its prevention.
- To promote rigorous study design and data analysis.
Main Methods:
- Utilizing motivating examples of selection on exposure levels.
- Employing causal diagrams to visualize relationships.
- Conducting simulations to test scenarios.
- Providing SAS and R code for reproducibility.
Main Results:
- Demonstrated specific scenarios where non-random selection does not lead to bias.
- Illustrated the dependence of bias occurrence on causal structure and estimand.
- Provided practical tools (code) for further investigation.
Conclusions:
- Non-random selection is not inherently biased; context is crucial.
- Identifying bias-free selection scenarios enhances understanding and application of causal inference.
- The study offers a foundation for designing more robust observational studies.
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