mlsanityData to 7 Oct 2026

Simple yet Sharp Sensitivity Analysis for Unmeasured Confounding

arXiv 2104.13020 ↗First posted 27 Apr 2021stat.ME cs.LG stat.MLcited by 5

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Abstract (arXiv)

We present a method for assessing the sensitivity of the true causal effect to unmeasured confounding. The method requires the analyst to set two intuitive parameters. Otherwise, the method is assumption-free. The method returns an interval that contains the true causal effect, and whose bounds are arbitrarily sharp, i.e. practically attainable. We show experimentally that our bounds can be tighter than those obtained by the method of Ding and VanderWeele (2016a) which, moreover, requires to set one more parameter than our method. Finally, we extend our method to bound the natural direct and indirect effects when there are measured mediators and unmeasured exposure-outcome confounding.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 2 of its 15 references that are in the map. First posted on arXiv 27 Apr 2021.

Builds on

2 of 15 references.

References · 2

  1. 2013Direct and Indirect Effectscited by 276
  2. 2016Sharp sensitivity bounds for mediation under unmeasured mediator-outcome confoundingcited by 2

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Citing papers per year: 2022: 2; 2023: 1; 2024: 1; 2025: 1.

5 papers.