View in EDS

Estimating policy effects in a social network with independent set sampling.

Saved in:
Bibliographic Details
Title: Estimating policy effects in a social network with independent set sampling.
Authors: Ang, Eugene T.Y. (AUTHOR), Bhattacharya, Prasanta (AUTHOR), Lim, Andrew E.B. (AUTHOR)
Source: Social Networks. May2025, Vol. 81, p17-30. 14p.
Subjects: NETWORK effect, TREATMENT effectiveness, INDEPENDENT sets, SOCIAL networks, STOCHASTIC models
Abstract: Evaluating the impact of policy interventions on respondents who are embedded in a social network is often challenging due to the presence of network interference within the treatment groups, as well as between treatment and non-treatment groups. In this paper, we propose a novel empirical strategy that combines network sampling based on the identification of independent sets with a stochastic actor-oriented model (SAOM) to infer the direct and net effects of a policy. By assigning respondents from an independent set to the treatment, we are able to block direct spillover of the treatment among the treated respondents for an extended period of time, during which the direct effect of the treatment can be isolated from the associated network interference. We empirically demonstrate this using a simulation-based evaluation of a fictitious policy implementation using both real-life and generated networks, and use a counterfactual approach to estimate the treatment effect of the policy. Our results highlight the effectiveness of our proposed empirical strategy, and notably, the role of network sampling techniques in influencing the evaluation of policy effects. The findings from this study have the potential to help researchers and policymakers with planning, designing, and anticipating policy responses in a networked society. • A policy evaluation technique that samples individuals from an independent set. • Leverages an SAOM and a counterfactual design to isolate direct policy effect from network effect. • Illustrative studies using real world and simulated network data to demonstrate effectiveness. • Offers a way to infer direct and net effect of interventions in a social network. [ABSTRACT FROM AUTHOR]
Copyright of Social Networks is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Regional Business News
FullText Text:
  Availability: 0
Header DbId: bwh
DbLabel: Regional Business News
An: 182902801
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Estimating policy effects in a social network with independent set sampling.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Ang%2C+Eugene+T%2EY%2E%22">Ang, Eugene T.Y.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bhattacharya%2C+Prasanta%22">Bhattacharya, Prasanta</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lim%2C+Andrew+E%2EB%2E%22">Lim, Andrew E.B.</searchLink> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Social+Networks%22">Social Networks</searchLink>. May2025, Vol. 81, p17-30. 14p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22NETWORK+effect%22">NETWORK effect</searchLink><br /><searchLink fieldCode="DE" term="%22TREATMENT+effectiveness%22">TREATMENT effectiveness</searchLink><br /><searchLink fieldCode="DE" term="%22INDEPENDENT+sets%22">INDEPENDENT sets</searchLink><br /><searchLink fieldCode="DE" term="%22SOCIAL+networks%22">SOCIAL networks</searchLink><br /><searchLink fieldCode="DE" term="%22STOCHASTIC+models%22">STOCHASTIC models</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Evaluating the impact of policy interventions on respondents who are embedded in a social network is often challenging due to the presence of network interference within the treatment groups, as well as between treatment and non-treatment groups. In this paper, we propose a novel empirical strategy that combines network sampling based on the identification of independent sets with a stochastic actor-oriented model (SAOM) to infer the direct and net effects of a policy. By assigning respondents from an independent set to the treatment, we are able to block direct spillover of the treatment among the treated respondents for an extended period of time, during which the direct effect of the treatment can be isolated from the associated network interference. We empirically demonstrate this using a simulation-based evaluation of a fictitious policy implementation using both real-life and generated networks, and use a counterfactual approach to estimate the treatment effect of the policy. Our results highlight the effectiveness of our proposed empirical strategy, and notably, the role of network sampling techniques in influencing the evaluation of policy effects. The findings from this study have the potential to help researchers and policymakers with planning, designing, and anticipating policy responses in a networked society. • A policy evaluation technique that samples individuals from an independent set. • Leverages an SAOM and a counterfactual design to isolate direct policy effect from network effect. • Illustrative studies using real world and simulated network data to demonstrate effectiveness. • Offers a way to infer direct and net effect of interventions in a social network. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Social Networks is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=bwh&AN=182902801
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.socnet.2024.10.002
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 14
        StartPage: 17
    Subjects:
      – SubjectFull: NETWORK effect
        Type: general
      – SubjectFull: TREATMENT effectiveness
        Type: general
      – SubjectFull: INDEPENDENT sets
        Type: general
      – SubjectFull: SOCIAL networks
        Type: general
      – SubjectFull: STOCHASTIC models
        Type: general
    Titles:
      – TitleFull: Estimating policy effects in a social network with independent set sampling.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Ang, Eugene T.Y.
      – PersonEntity:
          Name:
            NameFull: Bhattacharya, Prasanta
      – PersonEntity:
          Name:
            NameFull: Lim, Andrew E.B.
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 05
              Text: May2025
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 03788733
          Numbering:
            – Type: volume
              Value: 81
          Titles:
            – TitleFull: Social Networks
              Type: main
ResultId 1