Causal conclusions from observational studies may be sensitive to unmeasured confounding. In such cases, a sensitivity analysis is often conducted, which tries to infer the minimum amount of hidden bi

Taylor & Francis Group2025-02-10 更新10

A sensitivity analysis using Mantel-Haenszel (1959) bounds for variable child immunization.

儿童免疫效果评估

因果推断敏感性分析

Gamma: odds of differential assignment due to unobserved factors.Q_mh+: Mantel-Haenszel statistic (assumption: overestimation of treatment effect).Q_mh−: Mantel-Haenszel statistic (assumption: underes

Figshare2015-12-02 更新10

Assessing Sensitivity to Unconfoundedness: Estimation and Inference

因果推断敏感性分析

处理效应评估

This article provides a set of methods for quantifying the robustness of treatment effects estimated using the unconfoundedness assumption. Specifically, we estimate and do inference on bounds for var

Taylor & Francis Group2023-05-16 更新10

Bahadur Efficiency of Observational Block Designs

因果推断敏感性分析

观察性研究设计优化

To be convincing, an observational or nonrandomized study of causal effects must demonstrate that its conclusions cannot be readily explained by a small unmeasured bias in the way individuals were ass

Figshare2023-06-06 更新10

Sensitivity analyses testing Mendelian Randomization (MR) assumptions.

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