Properties of restricted randomization with implications for experimental design

by   Mattias Nordin, et al.

Recently, there as been an increasing interest in the use of heavily restricted randomization designs which enforces balance on observed covariates in randomized controlled trials. However, when restrictions are too strict, there is a risk that the treatment effect estimator will have a very high mean squared error. In this paper, we formalize this risk and propose a novel combinatoric-based approach to describe and address this issue. First, some known properties of complete randomization and restricted randomization are re-proven using basic combinatorics. Second, a novel diagnostic measure that only use the information embedded in the combinatorics of the design is proposed. Finally, we identify situations in which restricted designs can lead to an increased risk of getting a high mean squared error and discuss how our diagnostic measure can be used to detect and avoid such designs. Our results have implications for any restricted randomization design and can be used to evaluate the trade-off between enforcing balance on observed covariates and avoiding too restrictive designs.



page 1

page 2

page 3

page 4


Constrained randomization and statistical inference for multi-arm parallel cluster randomized controlled trials

Cluster randomized controlled trials (cRCTs) are designed to evaluate in...

Optimality of Matched-Pair Designs in Randomized Controlled Trials

In randomized controlled trials (RCTs), treatment is often assigned by s...

Improving the Power of the Randomization Test

We consider the problem of evaluating designs for a two-arm randomized e...

Model-assisted design of experiments in the presence of network correlated outcomes

We consider the problem of how to assign treatment in a randomized exper...

Online Balanced Experimental Design

e consider the experimental design problem in an online environment, an ...

Uncertainty Principles in Risk-Aware Statistical Estimation

We present a new uncertainty principle for risk-aware statistical estima...

On the Optimality of Randomization in Experimental Design: How to Randomize for Minimax Variance and Design-Based Inference

I study the minimax-optimal design for a two-arm controlled experiment w...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.