58 citations · 276 across the 26 of their papers we have counts for
13 papers · 1 filter
Randomization Tests for Adaptively Collected Data
Yash Nair, Lucas Janson
Randomization testing is a fundamental method in statistics, enabling inferential tasks such as testing for (conditional) independence of random variables, constructing confidence…
Powerful Partial Conjunction Hypothesis Testing via Conditioning
Biyonka Liang, Lu Zhang, Lucas Janson
A Partial Conjunction Hypothesis (PCH) test combines information across a set of base hypotheses to determine whether some subset is non-null. PCH tests arise in a diverse array of…
Surrogate-based global sensitivity analysis with statistical guarantees via floodgate
Massimo Aufiero, Lucas Janson
Computational models are utilized in many scientific domains to simulate complex systems. Sensitivity analysis is an important practice to aid our understanding of the mechanics of…
Controlled Discovery and Localization of Signals via Bayesian Linear Programming
Asher Spector, Lucas Janson
Scientists often must simultaneously localize and discover signals. For instance, in genetic fine-mapping, high correlations between nearby genetic variants make it hard to identif…
Using Machine Learning to Test Causal Hypotheses in Conjoint Analysis
Dae Woong Ham, Kosuke Imai, Lucas Janson
Conjoint analysis is a popular experimental design used to measure multidimensional preferences. Researchers examine how varying a factor of interest, while controlling for other r…
Powerful Knockoffs via Minimizing Reconstructability
Asher Spector, Lucas Janson
Model-X knockoffs allows analysts to perform feature selection using almost any machine learning algorithm while still provably controlling the expected proportion of false discove…
Floodgate: inference for model-free variable importance
Lu Zhang, Lucas Janson
Many modern applications seek to understand the relationship between an outcome variable and a covariate in the presence of a (possibly high-dimensional) confounding variab…
Fast and Powerful Conditional Randomization Testing via Distillation
Molei Liu, Eugene Katsevich, Lucas Janson +1
We consider the problem of conditional independence testing: given a response Y and covariates (X,Z), we test the null hypothesis that Y is independent of X given Z. The conditiona…
Relaxing the Assumptions of Knockoffs by Conditioning
Dongming Huang, Lucas Janson
The recent paper Candès et al. (2018) introduced model-X knockoffs, a method for variable selection that provably and non-asymptotically controls the false discovery rate with no r…
Metropolized Knockoff Sampling
Stephen Bates, Emmanuel Candès, Lucas Janson +1
Model-X knockoffs is a wrapper that transforms essentially any feature importance measure into a variable selection algorithm, which discovers true effects while rigorously control…
Familywise Error Rate Control via Knockoffs
Lucas Janson, Weijie Su
We present a novel method for controlling the -familywise error rate (-FWER) in the linear regression setting using the knockoffs framework first introduced by Barber and Can…
EigenPrism: Inference for High-Dimensional Signal-to-Noise Ratios
Lucas Janson, Rina Foygel Barber, Emmanuel Candès
Consider the following three important problems in statistical inference, namely, constructing confidence intervals for (1) the error of a high-dimensional () regression estim…
A Methodology for Robust Multiproxy Paleoclimate Reconstructions and Modeling of Temperature Conditional Quantiles
Lucas Janson, Bala Rajaratnam
Great strides have been made in the field of reconstructing past temperatures based on models relating temperature to temperature-sensitive paleoclimate proxies. One of the goals o…