We introduce a new data fusion method that utilizes multiple data source...
Debiased machine learning estimators for nonparametric inference of smoo...
We present estimators for smooth Hilbert-valued parameters, where smooth...
We propose causal isotonic calibration, a novel nonparametric method for...
Individualizing treatment assignment can improve outcomes for diseases w...
As a common step in refining their scientific inquiry, investigators are...
Estimation and evaluation of individualized treatment rules have been st...
This paper develops a new approach to post-selection inference for scree...
Though platform trials have been touted for their flexibility and stream...
We aim to make inferences about a smooth, finite-dimensional parameter b...
We study the performance of shape-constrained methods for evaluating imm...
In the absence of data from a randomized trial, researchers often aim to...
We present a general framework for using existing data to estimate the
e...
Gamma-minimax estimation is an approach to incorporate prior information...
We discuss the thought-provoking new objective functions for policy lear...
We study the stochastic convergence of the Cesàro mean of a sequence of
...
Suppose that we wish to estimate a finite-dimensional summary of one or ...
We frame the meta-learning of prediction procedures as a search for an
o...