Covariate Balancing Methods for Randomized Controlled Trials Are Not Adversarially Robust

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dc.contributor.committeeMemberBaraniuk, Richard G
dc.contributor.committeeMemberSegarra, Santiago
dc.contributor.committeeMemberSabharwal, Ashutosh
dc.creatorBabaei, Hossein
dc.date.accessioned2023-08-09T14:24:01Z
dc.date.created2023-05
dc.date.issued2023-06-20
dc.date.submittedMay 2023
dc.date.updated2023-08-09T14:24:01Z
dc.descriptionEMBARGO NOTE: This item is embargoed until 2025-05-01
dc.description.abstractThe 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 thesis, 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, iii we provide an optimization-based algorithm, namely Adversarial Treatment ASsignment in TREatment Effect Trials (ATASTREET), to find the adversarial treatment assignments.
dc.embargo.lift2025-05-01
dc.embargo.terms2025-05-01
dc.format.mimetypeapplication/pdf
dc.identifier.citationBabaei, Hossein. "Covariate Balancing Methods for Randomized Controlled Trials Are Not Adversarially Robust." (2023) Master’s Thesis, Rice University. <a href="https://hdl.handle.net/1911/115053">https://hdl.handle.net/1911/115053</a>.
dc.identifier.urihttps://hdl.handle.net/1911/115053
dc.language.isoeng
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.
dc.subjectCausal Inference
dc.subjectRandomized Controlled Trials
dc.subjectTreatment effect
dc.subjectCovariate Balancing
dc.titleCovariate Balancing Methods for Randomized Controlled Trials Are Not Adversarially Robust
dc.typeThesis
dc.type.materialText
thesis.degree.departmentElectrical and Computer Engineering
thesis.degree.disciplineEngineering
thesis.degree.grantorRice University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science
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