Search Results

Refine your results

Description:: Background: The treatment effect observed in randomized controlled trials may not reflect the actual effect in the target population if the trial and target populations …
Year:: 2023
Contributor:: Ojiambo, Rophence (creator); Steingrimsson, Jon (Advisor); Hogan, Joseph (Reader); Brown University. Department of Biostatistics (sponsor)
Genre:: theses
Subject:: causal inference; transportability; generalizability
Collection:: Public Health; Biostatistics; Theses and Dissertations

Full Record

Description:: The deep linear network (DLN) is a model for implicit regularization in gradient based optimization of overparametrized learning architectures. Training the DLN corresponds to a …
Year:: 2023
Contributor:: Veraszto, Zsolt (creator); Menon, Govind (Advisor); Cohen, Nadav (Reader); Darbon, Jerome (Reader); Brown University. Department of Applied Mathematics (sponsor)
Genre:: theses
Subject:: generalizability; gradient flows; implicit regularization; rieamannian geometry; matrix completion; deep linear network
Collection:: Applied Mathematics; Theses and Dissertations

Full Record

Description:: Estimating the average treatment effect (ATE) in the target population is often a main interest of epidemiologic studies. I consider three different ways to estimate …
Year:: 2021
Contributor:: Kim, Hongseok (creator); Dahabreh, Isssa (Advisor); Steingrimsson, Jon (Reader); Savitz, David (Reader); Brown University. Department of Epidemiology (sponsor)
Genre:: theses
Subject:: causal inference; missing data; heterogeneity of treatment effects; transportability; generalizability
Collection:: Public Health; Epidemiology; Theses and Dissertations

Full Record