There is a vast array of new and improved methods for comparing groups and studying
associations that offer the potential for substantially increasing power, providing improved …

Black-box machine learning models are now routinely used in high-risk settings, like
medical diagnostics, which demand uncertainty quantification to avoid consequential model …

Many studies in economics use instruments or treatments which combine a set of
exogenous shocks with other predetermined variables by a known formula. Examples …

This paper introduces the R package WRS2 that implements various robust statistical
methods. It elaborates on the basics of robust statistics by introducing robust location …

I follow The Design of Experiments, using randomization statistical inference to test the null
hypothesis of no treatment effects in a comprehensive sample of 53 experimental papers …

Social distancing is the core policy response to coronavirus disease 2019 (COVID-19). But,
as federal, state and local governments begin opening businesses and relaxing shelter-in …

In many fields of science, we observe a response variable together with a large number of
potential explanatory variables, and would like to be able to discover which variables are …

Agnostic notes on regression adjustments to experimental data: Reexamining Freedman's
critique Page 1 The Annals of Applied Statistics 2013, Vol. 7, No. 1, 295–318 DOI: 10.1214/12-AOAS583 …

We study estimation of causal effects in staggered-rollout designs—that is, settings where
there is staggered treatment adoption and the timing of treatment is as good as randomly …

Problems involving causal inference have dogged at the heels of statistics since its earliest
days. Correlation does not imply causation, and yet causal conclusions drawn from a …