arXiv subjects

Rana Jreich

Publications and source records attributed to Rana Jreich.

3 recordsLinked to original sources

arXiv2026

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

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.

stat.ME↗

arXiv2026

Calibrating confounding strength in sensitivity models for weighting estimators: a comparative review and a new method

Causal inference is only valid when its underlying assumptions are satisfied, one of the most central being the ignorability or unconfoundedness assumption. However, this hypothesis is often unrealistic in observational studies, as some confounding variables may remain unobserved. To address this limitation, sensitivity models for Inverse Probability Weighting (IPW) estimators, known as Marginal Sensitivity Models, have been introduced, allowing for a controlled relaxation of ignorability. A substantial body of literature has emerged around these models, aiming to derive sharp and robust bounds for both binary and continuous treatment effects. A key element of these approaches is the specification of a sensitivity parameter, referred to as the "confounding strength", which quantifies the extent of deviation from ignorability. Yet, determining an appropriate value for this parameter is challenging, and the final interpretation of sensitivity analyses can be unclear. We believe these difficulties represent major obstacles to the adoption of such methods in practice. Therefore, after introducing sensitivity analyses for IPW estimators, we review different strategies to estimate or lower bound the confounding strength, introduce a new method leveraging negative controls, provide a decision tree with guidelines to choose a suitable approach, and compare the methodologies in an in-depth simulation study.

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arXiv2025

Sharp Bounds for Continuous-Valued Treatment Effects with Unobserved Confounders

In causal inference, treatment effects are typically estimated under the ignorability, or unconfoundedness, assumption, which is often unrealistic in observational data. By relaxing this assumption and conducting a sensitivity analysis, we introduce novel bounds and derive confidence intervals for the Average Potential Outcome (APO) - a standard metric for evaluating continuous-valued treatment or exposure effects. We demonstrate that these bounds are sharp under a continuous sensitivity model, in the sense that they give the smallest possible interval under this model, and propose a doubly robust version of our estimators. In a comparative analysis with the method of Jesson et al. (2022) (arXiv:2204.10022), using both simulated and real datasets, we show that our approach not only yields sharper bounds but also achieves good coverage of the true APO, with significantly reduced computation times.

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