2 citations · 2 across the 4 of their papers we have counts for
5 papers
Identification and Estimation of Causal Estimands with Missing Not at Random Data
Faria Rauf Ria, Tarikul Islam, Mahbub A. H. M. Latif
Missing not at random (MNAR) data pose significant challenges for causal inference, particularly when both confounders and the outcome are partially observed. Without additional as…
A Unified Three-Stage Weighting Framework for Causal Inference and Mediation Analysis under Case-Control Sampling
Tarikul Islam, Mahbub A. H. M. Latif
Case-control studies are widely used in epidemiology and biomedical research because they provide substantial efficiency gains when outcomes are rare or prospective follow-up is im…
A Risk-Ratio-Based Marginal Sensitivity Model for Causal Effects in Observational Studies
Md Abdul Basit, Mahbub A. H. M. Latif, Abdus S Wahed
In observational studies, the identification of causal estimands depends on the no unmeasured confounding (NUC) assumption. As this assumption is not testable from observed data, s…
Sensitivity Analysis of Inverse Probability Weighting Estimators of Causal Effects in Observational Studies with Multivalued Treatments
Md Abdul Basit, Mahbub A. H. M. Latif, Abdus S Wahed
One of the fundamental challenges in drawing causal inferences from observational studies is that the assumption of no unmeasured confounding is not testable from observed data. Th…
A prediction criterion for working correlation structure selection in GEE
Gul Inan, Mahbub A. H. M. Latif, John Preisser
Generalized estimating equations (GEE) is one of the most commonly used methods for marginal regression analysis of longitudinal data, especially with discrete outcomes. The GEE me…