5 papers
ERICA: Quantifying Replicability of Cluster Analysis
Siamak K. Sorooshyari, Manuel A. Rivas, Robert Tibshirani
Despite being ubiquitous in science, clustering lacks a unified framework for quantitatively evaluating the replicability of its results. We present evaluating replicability via it…
Nonparametric Regression Discontinuity Designs with Survival Outcomes
Maximilian Schuessler, Erik Sverdrup, Robert Tibshirani +1
Quasi-experimental evaluations are central for generating real-world causal evidence and complementing insights from randomized trials. The regression discontinuity design (RDD) is…
Univariate-Guided Sparse Regression for Biobank-Scale High-Dimensional Omics Data
Joshua Richland, Tuomo Kiiskinen, William Wang +5
We present a scalable framework for computing polygenic risk scores (PRS) in high-dimensional genomic settings using the recently introduced Univariate-Guided Sparse Regression (un…
Lassoed Forests: Random Forests with Adaptive Lasso Post-selection
Jing Shang, James Bannon, Benjamin Haibe-Kains +1
Random forests are a statistical learning technique that use bootstrap aggregation to average high-variance and low-bias trees. Improvements to random forests, such as applying Las…
Statistical Learning for Heterogeneous Treatment Effects: Pretraining, Prognosis, and Prediction
Maximilian Schuessler, Erik Sverdrup, Robert Tibshirani
Robust estimation of heterogeneous treatment effects is a fundamental challenge for optimal decision-making in domains ranging from personalized medicine to educational policy. In…