Covariate Balancing Methods for Randomized Controlled Trials Are Not Adversarially Robust

Hossein Babaei, Sina Alemohammad, Richard G. Baraniuk

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


The first step toward 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. 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 article, we empirically show that covariate balancing with the standardized means difference (SMD) covariate balancing measure, as well as Pocock and Simon'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.

Original languageEnglish (US)
Pages (from-to)5014-5026
Number of pages13
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number4
StatePublished - Apr 1 2024


  • Adversarial analysis
  • causal effect
  • clinical trials
  • covariate balancing
  • econometric
  • experimental design
  • policy evaluation
  • randomized controlled trials (RCTs)
  • sequential treatment assignment
  • treatment effect

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications


Dive into the research topics of 'Covariate Balancing Methods for Randomized Controlled Trials Are Not Adversarially Robust'. Together they form a unique fingerprint.

Cite this