mlsanityData to 14 Oct 2026

Bounds and Sensitivity Analysis of the Causal Effect Under Outcome-Independent MNAR Confounding

arXiv 2410.06726 ↗First posted 9 Oct 2024stat.ME cs.LG stat.MLcited by 0

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Abstract (arXiv)

We report assumption-free bounds for any contrast between the probabilities of the potential outcome under exposure and non-exposure when the confounders are missing not at random. We assume that the missingness mechanism is outcome-independent. We also report a sensitivity analysis method to complement our bounds.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 10 references that are in the map. First posted on arXiv 9 Oct 2024.

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4 of 10 references.

References · 4

  1. 2018Graphical Models for Processing Missing Datacited by 58
  2. 2017Causal inference with confounders missing not at randomcited by 7
  3. 2023Identification and Estimation of Causal Effects with Confounders Missing Not at Randomcited by 1
  4. 2023Identification and Estimation for Nonignorable Missing Data: A Data Fusion Approachcited by 1

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