14 citations · 50 across the 20 of their papers we have counts for
7 papers · 1 filter
Qini Curve Estimation under Clustered Network Interference
Rickard Karlsson, Bram van den Akker, Felipe Moraes +2
Qini curves are a widely used tool for assessing treatment policies under allocation constraints as they visualize the incremental gain of a new treatment policy versus the cost of…
Falsification of Unconfoundedness by Testing Independence of Causal Mechanisms
Rickard K. A. Karlsson, Jesse H. Krijthe
A major challenge in estimating treatment effects in observational studies is the reliance on untestable conditions such as the assumption of no unmeasured confounding. In this wor…
Robust integration of external control data in randomized trials
Rickard Karlsson, Guanbo Wang, Piersilvio De Bartolomeis +2
One approach for increasing the efficiency of randomized trials is the use of "external controls" -- individuals who received the control treatment studied in the trial during rout…
The risks of risk assessment: causal blind spots when using prediction models for treatment decisions
Nan van Geloven, Ruth H Keogh, Wouter van Amsterdam +12
Clinicians increasingly rely on prediction models to guide treatment choices. Most prediction models, however, are developed using observational data that include some patients who…
When accurate prediction models yield harmful self-fulfilling prophecies
Wouter A. C. van Amsterdam, Nan van Geloven, Jesse H. Krijthe +2
Prediction models are popular in medical research and practice. By predicting an outcome of interest for specific patients, these models may help inform difficult treatment decisio…
Risk-based decision making: estimands for sequential prediction under interventions
Kim Luijken, Paweł Morzywołek, Wouter van Amsterdam +14
Prediction models are used amongst others to inform medical decisions on interventions. Typically, individuals with high risks of adverse outcomes are advised to undergo an interve…
Detecting hidden confounding in observational data using multiple environments
Rickard K. A. Karlsson, Jesse H. Krijthe
A common assumption in causal inference from observational data is that there is no hidden confounding. Yet it is, in general, impossible to verify this assumption from a single da…