2.5k citations · 9k across the 50 of their papers we have counts for
52 papers
Network Inference Using the Hub Model and Variants
Zhibing He, Yunpeng Zhao, Peter Bickel +3
Statistical network analysis primarily focuses on inferring the parameters of an observed network. In many applications, especially in the social sciences, the observed data is the…
Measures of independence and functional dependence
Peter J. Bickel
We follow up on Shi et al's (2020) and Cao's and my (2020) work on the local power of a new test for independence, Chatterjee (2019), and its relation to the local power properties…
Learning from learning machines: a new generation of AI technology to meet the needs of science
Luca Pion-Tonachini, Kristofer Bouchard, Hector Garcia Martin +33
We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery. The distinct goals of AI for industry versus the goals of AI for science cre…
Semiparametric Estimation of Treatment Effects in Randomized Experiments
Susan Athey, Peter J. Bickel, Aiyou Chen +2
We develop new semiparametric methods for estimating treatment effects. We focus on settings where the outcome distributions may be thick tailed, where treatment effects may be sma…
Binomial Mixture Model With U-shape Constraint
Yuting Ye, Peter J. Bickel
In this article, we study the binomial mixture model under the regime that the binomial size can be relatively large compared to the sample size . This project is motivated…
Interpretable Sensitivity Analysis for Balancing Weights
Dan Soriano, Eli Ben-Michael, Peter J. Bickel +2
Assessing sensitivity to unmeasured confounding is an important step in observational studies, which typically estimate effects under the assumption that all confounders are measur…