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PreprintarXiv licencearXiv

A simple sensitivity analysis for observational studies

  • Jeffrey Zhang
  • Dylan S. Small
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Abstract

Drawing causal conclusions from non-experimental (observational) data is a central aim in science but is extremely challenging. Without control over treatment assignment (as in an experiment), treated and control groups may not be comparable on unmeasured factors affecting treatment and outcome. To address such concerns, sensitivity analyses can be performed; sensitivity analyses acknowledge the existence of unmeasured factors but ask how strong they must be to overturn causal conclusions. We (re)introduce a particularly simple form of sensitivity analysis which can be performed by any reader of an observational study with access to only an estimate and confidence interval for a causal effect. This simple sensitivity analysis may serve as an effective tool for readers wishing to gauge the robustness of conclusions from an observational study.

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Cite

BibTeXRIS
BibTeX
@misc{zhang2026simple,
  title = {{A simple sensitivity analysis for observational studies}},
  author = {Jeffrey Zhang and Dylan S. Small},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.38270},
  eprint = {2610.38270},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.38270},
}
RIS
TY  - UNPB
TI  - A simple sensitivity analysis for observational studies
AU  - Jeffrey Zhang
AU  - Dylan S. Small
PY  - 2026
DA  - 2026/10/28
PB  - arXiv
DO  - 10.48550/arxiv.2610.38270
UR  - https://arxiv.org/abs/2610.38270
AB  - Drawing causal conclusions from non-experimental (observational) data is a central aim in science but is extremely challenging. Without control over treatment assignment (as in an experiment), treated and control groups may not be comparable on unmeasured factors affecting treatment and outcome. To address such concerns, sensitivity analyses can be performed; sensitivity analyses acknowledge the existence of unmeasured factors but ask how strong they must be to overturn causal conclusions. We (re)introduce a particularly simple form of sensitivity analysis which can be performed by any reader of an observational study with access to only an estimate and confidence interval for a causal effect. This simple sensitivity analysis may serve as an effective tool for readers wishing to gauge the robustness of conclusions from an observational study.
ER  -