34 citations · 35 across the 8 of their papers we have counts for
8 papers
Partial Identification Learning with Categorical Treatments for Individualized Treatment Rules
Johannes Hruza, Paweł Morzywołek, Jakob Zeitler +2
We develop a partial identification learning framework for individualized treatment rules (ITRs) with categorical treatments, outcomes, and instrumental variables. Rather than rely…
Deriving Complete Constraints in Hidden Variable Models
Michael C. Sachs, Erin E. Gabriel, Robin J. Evans +1
Hidden variable graphical models can sometimes imply constraints on the observable distribution that are more complex than simple conditional independence relations. These observab…
Bounds for causal mediation effects
Marie S. Breum, Vanessa Didelez, Erin E. Gabriel +1
Several frameworks have been proposed for studying causal mediation analysis. What these frameworks have in common is that they all make assumptions for point identifications that…
Adding covariates to bounds: What is the question?
Gustav Jonzon, Erin E Gabriel, Arvid Sjölander +1
Symbolic nonparametric bounds for partial identification of causal effects now have a long history in the causal literature. Sharp bounds, bounds that use all available information…
Improved small-sample inference for functions of parameters in the k-sample multinomial problem
Michael C Sachs, Erin E Gabriel, Michael P Fay
When the target parameter for inference is a real-valued, continuous function of probabilities in the -sample multinomial problem, variance estimation may be challenging. In sma…
The impact of coarsening an exposure on partial identifiability in instrumental variable settings
Erin E. Gabriel, Michael C. Sachs, Arvid Sjölander
In instrumental variable (IV) settings, such as in imperfect randomized trials and observational studies with Mendelian randomization, one may encounter a continuous exposure, the…
Propensity weighting plus adjustment in proportional hazards model is not doubly robust
Erin E Gabriel, Michael C Sachs, Ingeborg Waernbaum +5
Recently, it has become common for applied works to combine commonly used survival analysis modeling methods, such as the multivariable Cox model and propensity score weighting, wi…
Inverse probability of treatment weighting with generalized linear outcome models for doubly robust estimation
Erin E Gabriel, Michael C Sachs, Torben Martinussen +4
There are now many options for doubly robust estimation; however, there is a concerning trend in the applied literature to believe that the combination of a propensity score and an…