34 citations · 47 across the 11 of their papers we have counts for
7 papers · 1 filter
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…
Sharp symbolic nonparametric bounds for measures of benefit in observational and imperfect randomized studies with ordinal outcomes
Erin E Gabriel, Michael C Sachs, Andreas Kryger Jensen
The probability of benefit is a valuable and important measure of treatment effect, which has advantages over the average treatment effect. Particularly for an ordinal outcome, it…
Flexible evaluation of surrogacy in Bayesian adaptive platform studies
Michael C Sachs, Erin E Gabriel, Alessio Crippa +1
Trial level surrogates are useful tools for improving the speed and cost effectiveness of trials, but surrogates that have not been properly evaluated can cause misleading results.…
Sharp nonparametric bounds for decomposition effects with two binary mediators
Erin E Gabriel, Michael C Sachs, Arvid Sjölander
In randomized trials, once the total effect of the intervention has been estimated, it is often of interest to explore mechanistic effects through mediators along the causal pathwa…
A General Method for Deriving Tight Symbolic Bounds on Causal Effects
Michael C Sachs, Gustav Jonzon, Arvid Sjölander +1
A causal query will commonly not be identifiable from observed data, in which case no estimator of the query can be contrived without further assumptions or measured variables, reg…
Causal bounds for outcome-dependent sampling in observational studies
Erin E. Gabriel, Michael C. Sachs, Arvid Sjölander
Outcome-dependent sampling designs are common in many different scientific fields including epidemiology, ecology, and economics. As with all observational studies, such designs of…