Search Results for author:
Found 7 papers, 4 papers with code
Combining T-learning and DR-learning: a framework for oracle-efficient estimation of causal contrasts
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.
Adaptive debiased machine learning using data-driven model selection techniques
For this reason, practitioners may resort to simpler models based on parametric or semiparametric assumptions.
One-Step Estimation of Differentiable Hilbert-Valued Parameters
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.
Causal isotonic calibration for heterogeneous treatment effects
We propose causal isotonic calibration, a novel nonparametric method for calibrating predictors of heterogeneous treatment effects.
Adversarial Meta-Learning of Gamma-Minimax Estimators That Leverage Prior Knowledge
Bayes estimators are well known to provide a means to incorporate prior knowledge that can be expressed in terms of a single prior distribution.
Discussion of Kallus (2020) and Mo, Qi, and Liu (2020): New Objectives for Policy Learning
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.
Adversarial Monte Carlo Meta-Learning of Optimal Prediction Procedures
The Predictor's objective is to learn a function that maps from a new feature to an estimate of the associated outcome.
