Based on the physical randomization of completely randomized experiments, in a recent
article in Statistics in Medicine, Rigdon and Hudgens propose two approaches to obtaining …

Two methods are developed for constructing randomization‐based confidence sets for the
average effect of a treatment on a binary outcome. The methods are nonparametric and …

Estimation of causal effects in non‐randomized studies comprises two distinct phases:
design, without outcome data, and analysis of the outcome data according to a specified …

In clinical research, investigators are interested in inferring the average causal effect of a
treatment. However, the causal parameter that can be used to derive the average causal …

The estimation of causal effects has been the subject of extensive research. In
unconfounded studies with a dichotomous outcome, Y, Cangul, Chretien, Gutman and …

We propose a consistent estimator of sharp bounds on the variance of the difference-in-
means estimator in completely randomized experiments. Generalizing Robins [Stat. Med. 7 …

Randomisation inference (RI) is typically interpreted as testing Fisher's 'sharp'null
hypothesis that all unit-level effects are exactly zero. This hypothesis is often criticised as …

Causal inference in completely randomized treatment-control studies with binary outcomes
is discussed from Fisherian, Neymanian, and Bayesian perspectives, using the potential …

We discuss the computation of randomization tests for clinical trials of two treatments when
the primary outcome is based on a regression model. We begin by revisiting the seminal …

The estimation of causal effects in nonrandomized studies should comprise two distinct
phases: design, with no outcome data available; and analysis of the outcome data according …