53 citations · 56 across the 8 of their papers we have counts for
4 papers · 1 filter
The -test: Increasing the Linear Model -test's Power Under Sparsity Without Sacrificing Validity
Danielle Paulson, Souhardya Sengupta, Lucas Janson
We introduce a new procedure for testing the significance of a set of regression coefficients in a Gaussian linear model with . Our method, the -test, provides the sam…
Chiseling: Powerful and Valid Subgroup Selection via Interactive Machine Learning
Nathan Cheng, Asher Spector, Lucas Janson
In regression and causal inference, controlled subgroup selection aims to identify, with inferential guarantees, a subgroup (defined as a subset of the covariate space) on which th…
The Relative Instability of Model Comparison with Cross-validation
Alexandre Bayle, Lucas Janson, Lester Mackey
Cross-validation (CV) is known to provide asymptotically exact tests and confidence intervals for model improvement but only when the model comparison is relatively stable. Surpris…
Semiparametric Inference for Partially Identifiable Data Fusion Estimands via Double Machine Learning
Yicong Jiang, Lucas Janson
Many statistical estimands of interest (e.g., in regression or causality) are functions of the joint distribution of multiple random variables. But in some applications, data is no…