490 citations · 3.5k across the 123 of their papers we have counts for
7 papers · 2 filters
Data fission: splitting a single data point
James Leiner, Boyan Duan, Larry Wasserman +1
Suppose we observe a random vector from some distribution in a known family with unknown parameters. We ask the following question: when is it possible to split into tw…
Decorrelated Variable Importance
Isabella Verdinelli, Larry Wasserman
Because of the widespread use of black box prediction methods such as random forests and neural nets, there is renewed interest in developing methods for quantifying variable impor…
Universal Inference Meets Random Projections: A Scalable Test for Log-concavity
Robin Dunn, Aditya Gangrade, Larry Wasserman +1
Shape constraints yield flexible middle grounds between fully nonparametric and fully parametric approaches to modeling distributions of data. The specific assumption of log-concav…
Gaussian Universal Likelihood Ratio Testing
Robin Dunn, Aaditya Ramdas, Sivaraman Balakrishnan +1
The classical likelihood ratio test (LRT) based on the asymptotic chi-squared distribution of the log likelihood is one of the fundamental tools of statistical inference. A recent…
Causal Inference in the Time of Covid-19
Matteo Bonvini, Edward Kennedy, Valerie Ventura +1
In this paper we develop statistical methods for causal inference in epidemics. Our focus is in estimating the effect of social mobility on deaths in the Covid-19 pandemic. We prop…
Semiparametric counterfactual density estimation
Edward H. Kennedy, Sivaraman Balakrishnan, Larry Wasserman
Causal effects are often characterized with averages, which can give an incomplete picture of the underlying counterfactual distributions. Here we consider estimating the entire co…