Doubly valid and doubly sharp sensitivity analysis to unobserved confounding for survival outcomes

Jean-Baptiste Baitairian, Bernard Sebastien, Rana JREICH, Sandrine Katsahian, Agathe Guilloux
Proceedings of the 11th Machine Learning for Healthcare Conference, PMLR 340:168-213, 2026.

Abstract

Time-to-event outcomes are central in oncology and rare diseases, where treatment effects are often summarized by differences in survival curves or Restricted Mean Survival Time (RMST). In real-world data, estimating these causal effects relies on the absence of unobserved confounding, an assumption that is rarely satisfied. We develop a sensitivity analysis framework for causal treatment effects with survival outcomes under the Marginal Sensitivity Model (MSM). We introduce doubly valid and doubly sharp (DVDS) bounds for differences in survival functions and RMST, extending recent DVDS results to the time-to-event setting while accounting for informative censoring. In practice, our method yields tighter bounds and improved computational efficiency compared to a previous approach from the literature, on simulated and real data. For tractability, we assume independence between censoring and unobserved confounding, a limit that should be addressed in future works.

Cite this Paper

BibTeX
@InProceedings{pmlr-v340-baitairian26a, title = {Doubly valid and doubly sharp sensitivity analysis to unobserved confounding for survival outcomes}, author = {Baitairian, Jean-Baptiste and Sebastien, Bernard and JREICH, Rana and Katsahian, Sandrine and Guilloux, Agathe}, booktitle = {Proceedings of the 11th Machine Learning for Healthcare Conference}, pages = {168--213}, year = {2026}, editor = {Krishnan, Rahul G. and van Amsterdam, Wouter A. C. and Chopra, Sumit and Overgaard, Shauna and Hughes, Michael and Ötleş, Erkin and Shen, Yiqiu and Shanmugam, Divya and Nayan, Madhur and Engelhard, Matthew and Fackler, Jim and Oberst, Michael}, volume = {340}, series = {Proceedings of Machine Learning Research}, month = {12--14 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v340/main/assets/baitairian26a/baitairian26a.pdf}, url = {https://proceedings.mlr.press/v340/baitairian26a.html}, abstract = {Time-to-event outcomes are central in oncology and rare diseases, where treatment effects are often summarized by differences in survival curves or Restricted Mean Survival Time (RMST). In real-world data, estimating these causal effects relies on the absence of unobserved confounding, an assumption that is rarely satisfied. We develop a sensitivity analysis framework for causal treatment effects with survival outcomes under the Marginal Sensitivity Model (MSM). We introduce doubly valid and doubly sharp (DVDS) bounds for differences in survival functions and RMST, extending recent DVDS results to the time-to-event setting while accounting for informative censoring. In practice, our method yields tighter bounds and improved computational efficiency compared to a previous approach from the literature, on simulated and real data. For tractability, we assume independence between censoring and unobserved confounding, a limit that should be addressed in future works.} }
Endnote
%0 Conference Paper %T Doubly valid and doubly sharp sensitivity analysis to unobserved confounding for survival outcomes %A Jean-Baptiste Baitairian %A Bernard Sebastien %A Rana JREICH %A Sandrine Katsahian %A Agathe Guilloux %B Proceedings of the 11th Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2026 %E Rahul G. Krishnan %E Wouter A. C. van Amsterdam %E Sumit Chopra %E Shauna Overgaard %E Michael Hughes %E Erkin Ötleş %E Yiqiu Shen %E Divya Shanmugam %E Madhur Nayan %E Matthew Engelhard %E Jim Fackler %E Michael Oberst %F pmlr-v340-baitairian26a %I PMLR %P 168--213 %U https://proceedings.mlr.press/v340/baitairian26a.html %V 340 %X Time-to-event outcomes are central in oncology and rare diseases, where treatment effects are often summarized by differences in survival curves or Restricted Mean Survival Time (RMST). In real-world data, estimating these causal effects relies on the absence of unobserved confounding, an assumption that is rarely satisfied. We develop a sensitivity analysis framework for causal treatment effects with survival outcomes under the Marginal Sensitivity Model (MSM). We introduce doubly valid and doubly sharp (DVDS) bounds for differences in survival functions and RMST, extending recent DVDS results to the time-to-event setting while accounting for informative censoring. In practice, our method yields tighter bounds and improved computational efficiency compared to a previous approach from the literature, on simulated and real data. For tractability, we assume independence between censoring and unobserved confounding, a limit that should be addressed in future works.
APA
Baitairian, J., Sebastien, B., JREICH, R., Katsahian, S. & Guilloux, A.. (2026). Doubly valid and doubly sharp sensitivity analysis to unobserved confounding for survival outcomes. Proceedings of the 11th Machine Learning for Healthcare Conference, in Proceedings of Machine Learning Research 340:168-213 Available from https://proceedings.mlr.press/v340/baitairian26a.html.

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