Rerandomization with Diminishing Covariate Imbalance and Diverging Number of Covariates

by   Yuhao Wang, et al.

Completely randomized experiments have been the gold standard for drawing causal inference because they can balance all potential confounding on average. However, they can often suffer from unbalanced covariates for realized treatment assignments. Rerandomization, a design that rerandomizes the treatment assignment until a prespecified covariate balance criterion is met, has recently got attention due to its easy implementation, improved covariate balance and more efficient inference. Researchers have then suggested to use the assignments that minimize the covariate imbalance, namely the optimally balanced design. This has caused again the long-time controversy between two philosophies for designing experiments: randomization versus optimal and thus almost deterministic designs. Existing literature argued that rerandomization with overly balanced observed covariates can lead to highly imbalanced unobserved covariates, making it vulnerable to model misspecification. On the contrary, rerandomization with properly balanced covariates can provide robust inference for treatment effects while sacrificing some efficiency compared to the ideally optimal design. In this paper, we show it is possible that, by making the covariate imbalance diminishing at a proper rate as the sample size increases, rerandomization can achieve its ideally optimal precision that one can expect with perfectly balanced covariates while still maintaining its robustness. In particular, we provide the sufficient and necessary condition on the number of covariates for achieving the desired optimality. Our results rely on a more dedicated asymptotic analysis for rerandomization. The derived theory for rerandomization provides a deeper understanding of its large-sample property and can better guide its practical implementation. Furthermore, it also helps reconcile the controversy between randomized and optimal designs.



There are no comments yet.


page 1

page 2

page 3

page 4


Optimal Rerandomization via a Criterion that Provides Insurance Against Failed Experiments

We present an optimized rerandomization design procedure for a non-seque...

Conditional Balance Tests: Increasing Sensitivity and Specificity With Prognostic Covariates

Researchers often use covariate balance tests to assess whether a treatm...

Network Flow Methods for the Minimum Covariates Imbalance Problem

The problem of balancing covariates arises in observational studies wher...

A Comparison of Methods of Inference in Randomized Experiments from a Restricted Set of Allocations

Rerandomization is a strategy of increasing efficiency as compared to co...

Randomized and Balanced Allocation of Units into Treatment Groups Using the Finite Selection Model for R

The original Finite Selection Model (FSM) was developed in the 1970s to ...

Distributed Design for Causal Inferences on Big Observational Data

A fundamental issue in causal inference for Big Observational Data is co...

Rerandomization in 2^K Factorial Experiments

With many pretreatment covariates and treatment factors, the classical f...
This week in AI

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