Many existing transfer learning methods rely on leveraging information f...
Classical machine learning methods may lead to poor prediction performan...
This paper presents a comprehensive benchmarking suite tailored to offli...
TSCI implements treatment effect estimation from observational data unde...
Videos depict the change of complex dynamical systems over time in the f...
Safe reinforcement learning (RL) trains a constraint satisfaction policy...
Surrogate variables in electronic health records (EHR) play an important...
This paper presents a selective survey of recent developments in statist...
We present R software packages RobustIV and controlfunctionIV for causal...
Synthesizing information from multiple data sources is critical to ensur...
Safe reinforcement learning (RL) trains a policy to maximize the task re...
This paper proposes a new test of overidentifying restrictions (called t...
Instrumental variables regression is a popular causal inference method f...
This paper studies the problem of statistical inference for genetic
rela...
We introduce and illustrate through numerical examples the R package
wh...
Risk modeling with EHR data is challenging due to a lack of direct
obser...
Instrumental variable method is among the most commonly used causal infe...
Labeling patients in electronic health records with respect to their sta...
Heterogeneity is an important feature of modern data sets and a central ...
Instrumental variable methods are widely used for inferring the causal e...
Inferring causal relationships or related associations from observationa...
Group inference has been a long-standing question in statistics and the
...
Additive models, as a natural generalization of linear regression, have
...
The maximum correlation of functions of a pair of random variables is an...
The ability to predict individualized treatment effects (ITEs) based on ...
We consider statistical inference for the explained variance
β^Σβ under ...