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

Jose Peña
Proceedings of the Fourth Conference on Causal Learning and Reasoning, PMLR 275:693-703, 2025.

Abstract

We report distribution-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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v275-pena25a, title = {Bounds and Sensitivity Analysis of the Causal Effect Under Outcome-Independent MNAR Confounding}, author = {Pe\~{n}a, Jose}, booktitle = {Proceedings of the Fourth Conference on Causal Learning and Reasoning}, pages = {693--703}, year = {2025}, editor = {Huang, Biwei and Drton, Mathias}, volume = {275}, series = {Proceedings of Machine Learning Research}, month = {07--09 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v275/main/assets/pena25a/pena25a.pdf}, url = {https://proceedings.mlr.press/v275/pena25a.html}, abstract = {We report distribution-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.} }
Endnote
%0 Conference Paper %T Bounds and Sensitivity Analysis of the Causal Effect Under Outcome-Independent MNAR Confounding %A Jose Peña %B Proceedings of the Fourth Conference on Causal Learning and Reasoning %C Proceedings of Machine Learning Research %D 2025 %E Biwei Huang %E Mathias Drton %F pmlr-v275-pena25a %I PMLR %P 693--703 %U https://proceedings.mlr.press/v275/pena25a.html %V 275 %X We report distribution-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.
APA
Peña, J.. (2025). Bounds and Sensitivity Analysis of the Causal Effect Under Outcome-Independent MNAR Confounding. Proceedings of the Fourth Conference on Causal Learning and Reasoning, in Proceedings of Machine Learning Research 275:693-703 Available from https://proceedings.mlr.press/v275/pena25a.html.

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