34 citations · 56 across the 8 of their papers we have counts for
10 papers
Complementary strengths of the Neyman-Rubin and graphical causal frameworks
Tetiana Gorbach, Xavier de Luna, Juha Karvanen +1
This article contributes to the discussion on the relationship between the Neyman-Rubin and the graphical frameworks for causal inference. We present specific examples of data-gene…
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…
SelectionBias: An R Package for Bounding Selection Bias in Causal Estimands
Stina Zetterstrom, Ingeborg Waernbaum
Selection bias may arise when there are dropouts or missing data in the analysis, or when subjects are included or excluded in the analysis based upon some selection criteria for t…
Large Sample Properties of Entropy Balancing Estimators of Average Causal Effects
David Källberg, Ingeborg Waernbaum
Weighting methods are used in observational studies to adjust for covariate imbalances between treatment and control groups. Entropy balancing (EB) is an alternative to inverse pro…
Contrasting Identifying Assumptions of Average Causal Effects: Robustness and Semiparametric Efficiency
Tetiana Gorbach, Xavier de Luna, Juha Karvanen +1
Semiparametric inference on average causal effects from observational data is based on assumptions yielding identification of the effects. In practice, several distinct identifying…
Trial emulation and survival analysis for disease incidence registers: a case study on the causal effect of pre-emptive kidney transplantation
Camila Olarte Parra, Ingeborg Waernbaum, Staffan Schön +1
Numerous tutorials and research papers focus on methods in either survival analysis or causal inference, leaving common complications in medical studies unaddressed. In practice on…
Formulating causal questions and principled statistical answers
Els Goetghebeur, Saskia le Cessie, Bianca De Stavola +2
Although review papers on causal inference methods are now available, there is a lack of introductory overviews on what they can render and on the guiding criteria for choosing one…
Model misspecification and bias for inverse probability weighting and doubly robust estimators
Ingeborg Waernbaum, Laura Pazzagli
In the causal inference literature an estimator belonging to a class of semi-parametric estimators is called robust if it has desirable properties under the assumption that at leas…
Data-driven Algorithms for Dimension Reduction in Causal Inference
Emma Persson, Jenny Häggström, Ingeborg Waernbaum +1
In observational studies, the causal effect of a treatment may be confounded with variables that are related to both the treatment and the outcome of interest. In order to identify…