322 citations · 432 across the 23 of their papers we have counts for
5 papers · 1 filter
A Neural Framework for Generalized Causal Sensitivity Analysis
Dennis Frauen, Fergus Imrie, Alicia Curth +3
Unobserved confounding is common in many applications, making causal inference from observational data challenging. As a remedy, causal sensitivity analysis is an important tool to…
A U-turn on Double Descent: Rethinking Parameter Counting in Statistical Learning
Alicia Curth, Alan Jeffares, Mihaela van der Schaar
Conventional statistical wisdom established a well-understood relationship between model complexity and prediction error, typically presented as a U-shaped curve reflecting a trans…
Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Time
Toon Vanderschueren, Alicia Curth, Wouter Verbeke +1
Machine learning (ML) holds great potential for accurately forecasting treatment outcomes over time, which could ultimately enable the adoption of more individualized treatment str…
Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event Data
Alicia Curth, Mihaela van der Schaar
We study the problem of inferring heterogeneous treatment effects (HTEs) from time-to-event data in the presence of competing events. Albeit its great practical relevance, this pro…
In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimation
Alicia Curth, Mihaela van der Schaar
Personalized treatment effect estimates are often of interest in high-stakes applications -- thus, before deploying a model estimating such effects in practice, one needs to be sur…