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Found 4 papers, 3 papers with code
Multi-Study R-Learner for Estimating Heterogeneous Treatment Effects Across Studies Using Statistical Machine Learning
To this end, we propose a framework for multi-study HTE estimation that accounts for between-study heterogeneity in the nuisance functions and treatment effects.
Multi-Study Boosting: Theoretical Considerations for Merging vs. Ensembling
In the regression setting, we provide theoretical guidelines based on an analytical transition point to determine whether it is more beneficial to merge or to ensemble for boosting with linear learners.
Representation via Representations: Domain Generalization via Adversarially Learned Invariant Representations
We study an adversarial loss function for $k$ domains and precisely characterize its limiting behavior as $k$ grows, formalizing and proving the intuition, backed by experiments, that observing data from a larger number of domains helps.
Merging versus Ensembling in Multi-Study Prediction: Theoretical Insight from Random Effects
A critical decision point when training predictors using multiple studies is whether these studies should be combined or treated separately.
