4 citations · 4 across the 2 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…