We extend the randomization-based causal inference framework in Dasgupta et al.(2015)
for general 2 K factorial designs, and demonstrate the equivalence between regression …

We develop finite-population asymptotic theory for covariate adjustment in randomization-
based causal inference for 2 K factorial designs. In particular, we confirm that both the …

Randomization-based causal inference from split-plot designs Page 1 The Annals of Statistics
2018, Vol. 46, No. 5, 1876–1903 https://doi.org/10.1214/17-AOS1605 © Institute of Mathematical …

Factorial designs are widely used because of their ability to accommodate multiple factors
simultaneously. Factor-based regression with main effects and some interactions is the …

A framework for causal inference from two-level factorial designs is proposed, which uses
potential outcomes to define causal effects. The paper explores the effect of non-additivity of …

Neyman (1923/1990) introduced the randomization model, which contains the notation of
potential outcomes to define causal effects and a framework for large-sample inference …

Recently, there has been increasing interest in the use of heavily restricted randomization
designs which enforce balance on observed covariates in randomized controlled trials …

There are two general views in causal analysis of experimental data: the super population
view that the units are an independent sample from some hypothetical infinite population …

We establish the inferential properties of the mean‐difference estimator for the average
treatment effect in randomised experiments where each unit in a population is randomised …

The injunction to “analyze the way you randomize” is well known to statisticians since Fisher
advocated for randomization as the basis of inference. Yet even those convinced by the …