View in EDS

Data Visualization and Causal Reasoning Are Essential for Causal Effect Estimation

Saved in:
Bibliographic Details
Title: Data Visualization and Causal Reasoning Are Essential for Causal Effect Estimation
Language: English
Authors: Dan Chen (ORCID 0000-0003-4595-9971), Juan Juan Zhang, Henry S. Lynn
Source: Teaching Statistics: An International Journal for Teachers. 2026 48(3):160-171.
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 12
Publication Date: 2026
Document Type: Journal Articles
Reports - Research
Descriptors: Data, Visualization, Causal Models, Abstract Reasoning, Computation, Graphs, Data Analysis, Multiple Choice Tests, Inquiry, Active Learning
DOI: 10.1002/test.70031
ISSN: 0141-982X
1467-9639
Abstract: Causal effect estimation has gained much attention in recent years, and it is not uncommon to see graduate students incorporate these analyses into their research. However, students may not be aware how model assumptions and causal reasoning can critically influence their results. We borrowed a simple dataset that was previously used to demonstrate the use of directed acyclic graphs and designed a set of accompanying data analysis multiple-choice questions to engage students in inquiry-based learning, guiding them to discover the importance of data visualization and subject-specific causal reasoning for assessing different methods of causal effect estimation.
Abstractor: As Provided
Entry Date: 20260101
Accession Number: EJ1516728
Database: ERIC
FullText Text:
  Availability: 0
Header DbId: eric
DbLabel: ERIC
An: EJ1516728
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Data Visualization and Causal Reasoning Are Essential for Causal Effect Estimation
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Dan+Chen%22">Dan Chen</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-4595-9971">0000-0003-4595-9971</externalLink>)<br /><searchLink fieldCode="AR" term="%22Juan+Juan+Zhang%22">Juan Juan Zhang</searchLink><br /><searchLink fieldCode="AR" term="%22Henry+S%2E+Lynn%22">Henry S. Lynn</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Teaching+Statistics%3A+An+International+Journal+for+Teachers%22"><i>Teaching Statistics: An International Journal for Teachers</i></searchLink>. 2026 48(3):160-171.
– Name: Avail
  Label: Availability
  Group: Avail
  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 12
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2026
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Data%22">Data</searchLink><br /><searchLink fieldCode="DE" term="%22Visualization%22">Visualization</searchLink><br /><searchLink fieldCode="DE" term="%22Causal+Models%22">Causal Models</searchLink><br /><searchLink fieldCode="DE" term="%22Abstract+Reasoning%22">Abstract Reasoning</searchLink><br /><searchLink fieldCode="DE" term="%22Computation%22">Computation</searchLink><br /><searchLink fieldCode="DE" term="%22Graphs%22">Graphs</searchLink><br /><searchLink fieldCode="DE" term="%22Data+Analysis%22">Data Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Multiple+Choice+Tests%22">Multiple Choice Tests</searchLink><br /><searchLink fieldCode="DE" term="%22Inquiry%22">Inquiry</searchLink><br /><searchLink fieldCode="DE" term="%22Active+Learning%22">Active Learning</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1002/test.70031
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0141-982X<br />1467-9639
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Causal effect estimation has gained much attention in recent years, and it is not uncommon to see graduate students incorporate these analyses into their research. However, students may not be aware how model assumptions and causal reasoning can critically influence their results. We borrowed a simple dataset that was previously used to demonstrate the use of directed acyclic graphs and designed a set of accompanying data analysis multiple-choice questions to engage students in inquiry-based learning, guiding them to discover the importance of data visualization and subject-specific causal reasoning for assessing different methods of causal effect estimation.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 20260101
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1516728
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1516728
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1002/test.70031
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 12
        StartPage: 160
    Subjects:
      – SubjectFull: Data
        Type: general
      – SubjectFull: Visualization
        Type: general
      – SubjectFull: Causal Models
        Type: general
      – SubjectFull: Abstract Reasoning
        Type: general
      – SubjectFull: Computation
        Type: general
      – SubjectFull: Graphs
        Type: general
      – SubjectFull: Data Analysis
        Type: general
      – SubjectFull: Multiple Choice Tests
        Type: general
      – SubjectFull: Inquiry
        Type: general
      – SubjectFull: Active Learning
        Type: general
    Titles:
      – TitleFull: Data Visualization and Causal Reasoning Are Essential for Causal Effect Estimation
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Dan Chen
      – PersonEntity:
          Name:
            NameFull: Juan Juan Zhang
      – PersonEntity:
          Name:
            NameFull: Henry S. Lynn
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 10
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 0141-982X
            – Type: issn-electronic
              Value: 1467-9639
          Numbering:
            – Type: volume
              Value: 48
            – Type: issue
              Value: 3
          Titles:
            – TitleFull: Teaching Statistics: An International Journal for Teachers
              Type: main
ResultId 1