Covariate Balancing Methods for Randomized Controlled Trials Are Not Adversarially Robust

TL;DR

This paper demonstrates that covariate balancing methods like SMD and Pocock's are vulnerable to worst-case treatment assignments, and introduces an adversarial attack method to identify such cases, highlighting potential reliability issues in RCTs.

Contribution

The paper reveals the susceptibility of common covariate balancing methods to adversarial treatment assignments and proposes an optimization-based attack algorithm, AASTREET, to find worst-case scenarios.

Findings

Covariate balancing methods can be exploited by worst-case treatment assignments.

An adversarial attack algorithm, AASTREET, effectively finds worst-case treatment assignments.

The study highlights the need for robustness checks in covariate balancing methods.

Abstract

The first step towards investigating the effectiveness of a treatment via a randomized trial is to split the population into control and treatment groups then compare the average response of the treatment group receiving the treatment to the control group receiving the placebo. In order to ensure that the difference between the two groups is caused only by the treatment, it is crucial that the control and the treatment groups have similar statistics. Indeed, the validity and reliability of a trial are determined by the similarity of two groups' statistics. Covariate balancing methods increase the similarity between the distributions of the two groups' covariates. However, often in practice, there are not enough samples to accurately estimate the groups' covariate distributions. In this paper, we empirically show that covariate balancing with the Standardized Means Difference (SMD) covariate balancing measure, as well as Pocock's sequential treatment assignment method, are susceptible to worst-case treatment assignments. Worst-case treatment assignments are those admitted by the covariate balance measure, but result in highest possible ATE estimation errors. We developed an adversarial attack to find adversarial treatment assignment for any given trial. Then, we provide an index to measure how close the given trial is to the worst-case. To this end, we provide an optimization-based algorithm, namely Adversarial Treatment ASsignment in TREatment Effect Trials (ATASTREET), to find the adversarial treatment assignments.