STATISTICAL SCIENCE

Comment: Spherical Cows in a Vacuum: Data Analysis Competitions for Causal Inference

Comment: Strengthening Empirical Evaluation of Causal Inference Methods

Comment: Will Competition-Winning Methods for Causal Inference Also Succeed in Practice?

Comment: Unreasonable Effectiveness of Monte Carlo

Rejoinder: On nearly assumption-free tests of nominal confidence interval coverage for causal parameters estimated by machine learning

Models as Approximations—Rejoinder

Larry Brown’s Work on Admissibility

Gaussianization Machines for Non-Gaussian Function Estimation Models

Producing Official County-Level Agricultural Estimates in the United States: Needs and Challenges

Comment on “Automated Versus Do-It-Yourself Methods for Causal Inference: Lessons Learned from a Data Analysis Competition”

Comment: Empirical Bayes Interval Estimation

Comment: Variational Autoencoders as Empirical Bayes

Comment: Statistical Inference from a Predictive Perspective

Gaussian integrals and Rice series in crossing distributions : to compute the distribution of maxima and other features of Gaussian processes

A Conversation with Piet Groeneboom

A Conversation with Robert E. Kass

A Conversation with Noel Cressie

Larry Brown’s Contributions to Parametric Inference, Decision Theory and Foundations: A Survey

Comment: Models Are Approximations!

A Conversation with Peter Diggle

Statistical Theory Powering Data Science

A Kernel Regression Procedure in the 3D Shape Space with an Application to Online Sales of Children’s Wear

Rejoinder: Response to Discussions and a Look Ahead

Comment: Causal Inference Competitions: Where Should We Aim?

Discussion of Models as Approximations I & II

A Conversation with Dick Dudley

Comment on Models as Approximations, Parts I and II, by Buja et al.

Comment: Contributions of Model Features to BART Causal Inference Performance Using ACIC 2016 Competition Data