no code implementations • 3 Feb 2024 • 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 conditional relative risk.
no code implementations • 24 Jul 2023 • Lars van der Laan, Marco Carone, Alex Luedtke, Mark van der Laan
For this reason, practitioners may resort to simpler models based on parametric or semiparametric assumptions.
1 code implementation • 27 Feb 2023 • Lars van der Laan, Ernesto Ulloa-Pérez, Marco Carone, Alex Luedtke
We propose causal isotonic calibration, a novel nonparametric method for calibrating predictors of heterogeneous treatment effects.
3 code implementations • 7 Apr 2020 • Brian D. Williamson, Peter B. Gilbert, Noah R. Simon, Marco Carone
In many applications, it is of interest to assess the relative contribution of features (or subsets of features) toward the goal of predicting a response -- in other words, to gauge the variable importance of features.
1 code implementation • ICML 2018 • Jean Feng, Brian Williamson, Noah Simon, Marco Carone
In predictive modeling applications, it is often of interest to determine the relative contribution of subsets of features in explaining the variability of an outcome.
1 code implementation • 6 May 2017 • Alexander R. Luedtke, Oleg Sofrygin, Mark J. Van Der Laan, Marco Carone
Consider estimating the G-formula for the counterfactual mean outcome under a given treatment regime in a longitudinal study.
Methodology
no code implementations • 14 Oct 2015 • Alexander R. Luedtke, Marco Carone, Mark J. Van Der Laan
We present a novel family of nonparametric omnibus tests of the hypothesis that two unknown but estimable functions are equal in distribution when applied to the observed data structure.