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Combining Observational and Experimental Data for Causal Inference Considering Data Privacy

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Title: Combining Observational and Experimental Data for Causal Inference Considering Data Privacy
Language: English
Authors: Charlotte Z. Mann, Adam C. Sales, Johann A. Gagnon-Bartsch
Source: Grantee Submission. 2025 13.
Peer Reviewed: Y
Page Count: 23
Publication Date: 2025
Sponsoring Agency: Institute of Education Sciences (ED)
National Science Foundation (NSF), Division of Mathematical Sciences (DMS)
Contract Number: R305D210031
1646108
Document Type: Journal Articles
Reports - Research
Descriptors: Causal Models, Statistical Analysis, Privacy, Risk, Information Security, Confidentiality, Comparative Analysis, Generalization, Simulation, Disclosure, Randomized Controlled Trials, Algorithms, Administration, Statistical Inference
DOI: 10.1515/jci-2022-0081
Abstract: Combining observational and experimental data for causal inference can improve treatment effect estimation. However, many observational data sets cannot be released due to data privacy considerations, so one researcher may not have access to both experimental and observational data. Nonetheless, a small amount of risk of disclosing sensitive information might be tolerable to organizations that house confidential data. In these cases, organizations can employ data privacy techniques, which decrease disclosure risk, potentially at the expense of data utility. In this paper, we explore disclosure limiting transformations of observational data, which can be combined with experimental data to estimate the sample and population average treatment effects. We consider leveraging observational data to improve generalizability of treatment effect estimates when a randomized experiment (RCT) is not representative of the population of interest, and to increase precision of treatment effect estimates. Through simulation studies, we illustrate the trade-off between privacy and utility when employing different disclosure limiting transformations. We find that leveraging transformed observational data in treatment effect estimation can still improve estimation over only using data from an RCT.
Abstractor: As Provided
IES Funded: Yes
Entry Date: 2025
Accession Number: ED670978
Database: ERIC
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  Data: Combining observational and experimental data for causal inference can improve treatment effect estimation. However, many observational data sets cannot be released due to data privacy considerations, so one researcher may not have access to both experimental and observational data. Nonetheless, a small amount of risk of disclosing sensitive information might be tolerable to organizations that house confidential data. In these cases, organizations can employ data privacy techniques, which decrease disclosure risk, potentially at the expense of data utility. In this paper, we explore disclosure limiting transformations of observational data, which can be combined with experimental data to estimate the sample and population average treatment effects. We consider leveraging observational data to improve generalizability of treatment effect estimates when a randomized experiment (RCT) is not representative of the population of interest, and to increase precision of treatment effect estimates. Through simulation studies, we illustrate the trade-off between privacy and utility when employing different disclosure limiting transformations. We find that leveraging transformed observational data in treatment effect estimation can still improve estimation over only using data from an RCT.
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        Value: 10.1515/jci-2022-0081
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 23
    Subjects:
      – SubjectFull: Causal Models
        Type: general
      – SubjectFull: Statistical Analysis
        Type: general
      – SubjectFull: Privacy
        Type: general
      – SubjectFull: Risk
        Type: general
      – SubjectFull: Information Security
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      – SubjectFull: Confidentiality
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      – SubjectFull: Generalization
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      – SubjectFull: Simulation
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      – SubjectFull: Disclosure
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      – SubjectFull: Randomized Controlled Trials
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      – SubjectFull: Algorithms
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      – SubjectFull: Administration
        Type: general
      – SubjectFull: Statistical Inference
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      – TitleFull: Combining Observational and Experimental Data for Causal Inference Considering Data Privacy
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            NameFull: Adam C. Sales
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            NameFull: Johann A. Gagnon-Bartsch
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