1 code implementation • 14 May 2024 • Alex Luedtke
We introduce an algorithm that simplifies the construction of efficient estimators, making them accessible to a broader audience.
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 • 29 Mar 2023 • Alex Luedtke, Incheoul Chung
When the parameter space is a reproducing kernel Hilbert space, we provide a means to obtain efficient, root-n rate estimators and corresponding confidence sets.
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.
2 code implementations • 10 Dec 2020 • Hongxiang Qiu, Alex Luedtke
Bayes estimators are well known to provide a means to incorporate prior knowledge that can be expressed in terms of a single prior distribution.
no code implementations • 9 Oct 2020 • Sijia Li, Xiudi Li, Alex Luedtke
We discuss the thought-provoking new objective functions for policy learning that were proposed in "More efficient policy learning via optimal retargeting" by Nathan Kallus and "Learning optimal distributionally robust individualized treatment rules" by Weibin Mo, Zhengling Qi, and Yufeng Liu.
1 code implementation • 26 Feb 2020 • Alex Luedtke, Incheoul Chung, Oleg Sofrygin
The Predictor's objective is to learn a function that maps from a new feature to an estimate of the associated outcome.