11 citations · 21 across the 6 of their papers we have counts for
6 papers
Multi-Study R-Learner for Estimating Heterogeneous Treatment Effects Across Studies Using Statistical Machine Learning
Cathy Shyr, Boyu Ren, Prasad Patil +1
Estimating heterogeneous treatment effects (HTEs) is crucial for precision medicine. While multiple studies can improve the generalizability of results, leveraging them for estimat…
Multi-Study Boosting: Theoretical Considerations for Merging vs. Ensembling
Cathy Shyr, Pragya Sur, Giovanni Parmigiani +1
Cross-study replicability is a powerful model evaluation criterion that emphasizes generalizability of predictions. When training cross-study replicable prediction models, it is cr…
Cross-validation Approaches for Multi-study Predictions
Boyu Ren, Prasad Patil, Francesca Dominici +2
We consider prediction in multiple studies with potential differences in the relationships between predictors and outcomes. Our objective is to integrate data from multiple studies…
Representation via Representations: Domain Generalization via Adversarially Learned Invariant Representations
Zhun Deng, Frances Ding, Cynthia Dwork +4
We investigate the power of censoring techniques, first developed for learning {\em fair representations}, to address domain generalization. We examine {\em adversarial} censoring…
Merging versus Ensembling in Multi-Study Prediction: Theoretical Insight from Random Effects
Zoe Guan, Giovanni Parmigiani, Prasad Patil
A critical decision point when training predictors using multiple studies is whether studies should be combined or treated separately. We compare two multi-study prediction approac…
A glass half full interpretation of the replicability of psychological science
Jeffrey T. Leek, Prasad Patil, Roger D. Peng
A recent study of the replicability of key psychological findings is a major contribution toward understanding the human side of the scientific process. Despite the careful and nua…