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1. Design sensitivity and its implications for weighted observational studies

   https://doi.org/10.1093/jrsssa/qnaf067  

   Huang, Melody; Soriano, Dan; Pimentel, Samuel D (July 2025, Journal of the Royal Statistical Society Series A: Statistics in Society)

   Abstract Careful design and preregistration of a treated-control comparison in an observational study enhances the quality of its evidence. However, sensitivity to unmeasured confounding is not typically a primary consideration in the preanalysis design stage. In the following paper, we introduce a framework for weighted estimators that allows researchers to optimize for robustness to omitted variable bias at the design stage using a measure called design sensitivity. Inspired by a similar measure for matching estimators, design sensitivity describes the asymptotic power of a sensitivity analysis, and allows researchers to transparently evaluate the impact of different estimation strategies on sensitivity to omitted confounders prior to outcome analysis. We apply this general framework to two commonly used sensitivity models, the marginal sensitivity model and the variance-based sensitivity model. By comparing design sensitivities, we interrogate how key features of weighted observational designs, including trimming weights, choosing between different treatment versions, and altering the study’s inclusion criteria, impact robustness to unmeasured confounding. We illustrate the proposed framework on a study examining drivers of support for the 2016 Colombian peace agreement. 

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2. Semiparametric estimation of structural failure time models in continuous-time processes

   https://doi.org/10.1093/biomet/asz057  

   Yang, S; Pieper, K; Cools, F (October 2019, Biometrika)

   Summary Structural failure time models are causal models for estimating the effect of time-varying treatments on a survival outcome. G-estimation and artificial censoring have been proposed for estimating the model parameters in the presence of time-dependent confounding and administrative censoring. However, most existing methods require manually pre-processing data into regularly spaced data, which may invalidate the subsequent causal analysis. Moreover, the computation and inference are challenging due to the nonsmoothness of artificial censoring. We propose a class of continuous-time structural failure time models that respects the continuous-time nature of the underlying data processes. Under a martingale condition of no unmeasured confounding, we show that the model parameters are identifiable from a potentially infinite number of estimating equations. Using the semiparametric efficiency theory, we derive the first semiparametric doubly robust estimators, which are consistent if the model for the treatment process or the failure time model, but not necessarily both, is correctly specified. Moreover, we propose using inverse probability of censoring weighting to deal with dependent censoring. In contrast to artificial censoring, our weighting strategy does not introduce nonsmoothness in estimation and ensures that resampling methods can be used for inference. 

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3. Matched design for marginal causal effect on restricted mean survival time in observational studies

   https://doi.org/10.1515/jci-2022-0035  

   Lin, Zihan; Ni, Ai; Lu, Bo (January 2023, Journal of Causal Inference)

   Abstract Investigating the causal relationship between exposure and time-to-event outcome is an important topic in biomedical research. Previous literature has discussed the potential issues of using hazard ratio (HR) as the marginal causal effect measure due to noncollapsibility. In this article, we advocate using restricted mean survival time (RMST) difference as a marginal causal effect measure, which is collapsible and has a simple interpretation as the difference of area under survival curves over a certain time horizon. To address both measured and unmeasured confounding, a matched design with sensitivity analysis is proposed. Matching is used to pair similar treated and untreated subjects together, which is generally more robust than outcome modeling due to potential misspecifications. Our propensity score matched RMST difference estimator is shown to be asymptotically unbiased, and the corresponding variance estimator is calculated by accounting for the correlation due to matching. Simulation studies also demonstrate that our method has adequate empirical performance and outperforms several competing methods used in practice. To assess the impact of unmeasured confounding, we develop a sensitivity analysis strategy by adapting the E -value approach to matched data. We apply the proposed method to the Atherosclerosis Risk in Communities Study (ARIC) to examine the causal effect of smoking on stroke-free survival. 

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4. Interpretable sensitivity analysis for balancing weights

   https://doi.org/10.1093/jrsssa/qnad032  

   Soriano, Dan; Ben-Michael, Eli; Bickel, Peter J; Feller, Avi; Pimentel, Samuel D (March 2023, Journal of the Royal Statistical Society Series A: Statistics in Society)

   Abstract Assessing sensitivity to unmeasured confounding is an important step in observational studies, which typically estimate effects under the assumption that all confounders are measured. In this paper, we develop a sensitivity analysis framework for balancing weights estimators, an increasingly popular approach that solves an optimization problem to obtain weights that directly minimizes covariate imbalance. In particular, we adapt a sensitivity analysis framework using the percentile bootstrap for a broad class of balancing weights estimators. We prove that the percentile bootstrap procedure can, with only minor modifications, yield valid confidence intervals for causal effects under restrictions on the level of unmeasured confounding. We also propose an amplification—a mapping from a one-dimensional sensitivity analysis to a higher dimensional sensitivity analysis—to allow for interpretable sensitivity parameters in the balancing weights framework. We illustrate our method through extensive real data examples. 

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5. Simulation-Based Sensitivity Analysis in Optimal Treatment Regimes and Causal Decomposition With Individualized Interventions

   https://doi.org/10.1177/00491241251377741  

   Park, Soojin; Kang, Suyeon; Lee, Chioun (September 2025, Sociological Methods & Research)

   Causal decomposition analysis aims to assess the effect of modifying risk factors on reducing social disparities in outcomes. Recently, this analysis has incorporated individual characteristics when modifying risk factors by utilizing optimal treatment regimes (OTRs). Since the newly defined individualized effects rely on the no omitted confounding assumption, developing sensitivity analyses to account for potential omitted confounding is essential. Moreover, OTRs and individualized effects are primarily based on binary risk factors, and no formal approach currently exists to benchmark the strength of omitted confounding using observed covariates for binary risk factors. To address this gap, we extend a simulation-based sensitivity analysis that simulates unmeasured confounders, addressing two sources of bias emerging from deriving OTRs and estimating individualized effects. Additionally, we propose a formal bounding strategy that benchmarks the strength of omitted confounding for binary risk factors. Using the High School Longitudinal Study 2009 (HSLS:09), we demonstrate this sensitivity analysis and benchmarking method. 

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