4 papers
G-computation for causal effect estimation from observational hierarchical data with unmeasured cluster context
Shafayet Khan Shafee, Bishal Sarker, Md. Niamul Islam Sium
Observational studies frequently involve hierarchical data structures in which individuals are nested within higher-level units. In such settings, unmeasured cluster-level factors…
Causal Inference with MNAR Self-Masking Confounders: A Stratified Delta-Imputed Propensity Estimation Method
Md. Niamul Islam Sium, Mohammad Hridoy Patwary
In observational studies, causal inference becomes difficult when confounders are missing-not-at-random (MNAR), particularly where the missingness depends on the confounder's own u…
Quantifying Robustness to Unmeasured Confounding in Time-Varying Treatment Confounder Settings: An Extension of E-value Approach
Md. Niamul Islam Sium
Background: The E-value has become widely used for assessing robustness to unmeasured confounding in observational studies, but the original framework was developed for single time…
Central limit theorem for the global clustering coefficient of random geometric graphs
Mingao Yuan, Md. Niamul Islam Sium
The global clustering coefficient serves as a powerful metric for the structural analysis and comparison of complex networks. Random geometric graphs offer a realistic framework fo…