74 citations · 120 across the 18 of their papers we have counts for
21 papers
Combining T-learning and DR-learning: a framework for oracle-efficient estimation of causal contrasts
Lars van der Laan, Marco Carone, Alex Luedtke
We introduce efficient plug-in (EP) learning, a novel framework for the estimation of heterogeneous causal contrasts, such as the conditional average treatment effect and condition…
Adaptive debiased machine learning using data-driven model selection techniques
Lars van der Laan, Marco Carone, Alex Luedtke +1
Debiased machine learning estimators for smooth functionals in nonparametric models can exhibit substantial variability and instability, often leading practitioners to instead rely…
Causal isotonic calibration for heterogeneous treatment effects
Lars van der Laan, Ernesto Ulloa-Pérez, Marco Carone +1
We propose causal isotonic calibration, a novel nonparametric method for calibrating predictors of heterogeneous treatment effects. Furthermore, we introduce cross-calibration, a d…
A framework for leveraging machine learning tools to estimate personalized survival curves
Charles J. Wolock, Peter B. Gilbert, Noah Simon +1
The conditional survival function of a time-to-event outcome subject to censoring and truncation is a common target of estimation in survival analysis. This parameter may be of sci…
Can the potential benefit of individualizing treatment be assessed using trial summary statistics alone?
Nina Galanter, Marco Carone, Ronald C. Kessler +1
Individualizing treatment assignment can improve outcomes for diseases with patient-to-patient variability in comparative treatment effects. When a clinical trial demonstrates that…
A general adaptive framework for multivariate point null testing
Adam Elder, Marco Carone, Peter Gilbert +1
As a common step in refining their scientific inquiry, investigators are often interested in performing some screening of a collection of given statistical hypotheses. For example,…