Causal Priors and Their Influence on Judgements of Causality in Visualized Data

“Correlation does not imply causation” is a famous mantra in statistical and visual analysis. However, consumers of visualizations often draw causal conclusions when only correlations between variables are shown. In this paper, we investigate factors that contribute to causal relationships users per...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	IEEE transactions on visualization and computer graphics Ročník 31; číslo 1; s. 765 - 775
Hlavní autori:	Wang, Arran Zeyu, Borland, David, Peck, Tabitha C., Wang, Wenyuan, Gotz, David
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.01.2025
Predmet:	Association Bars Causal inference Causal prior Causality Cause effect analysis Cognition Correlation Data visualization Guidelines Perception and cognition Visualization
ISSN:	1077-2626, 1941-0506, 1941-0506
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Abstract	“Correlation does not imply causation” is a famous mantra in statistical and visual analysis. However, consumers of visualizations often draw causal conclusions when only correlations between variables are shown. In this paper, we investigate factors that contribute to causal relationships users perceive in visualizations. We collected a corpus of concept pairs from variables in widely used datasets and created visualizations that depict varying correlative associations using three typical statistical chart types. We conducted two MTurk studies on (1) preconceived notions on causal relations without charts, and (2) perceived causal relations with charts, for each concept pair. Our results indicate that people make assumptions about causal relationships between pairs of concepts even without seeing any visualized data. Moreover, our results suggest that these assumptions constitute causal priors that, in combination with visualized association, impact how data visualizations are interpreted. The results also suggest that causal priors may lead to over- or under-estimation in perceived causal relations in different circumstances, and that those priors can also impact users' confidence in their causal assessments. In addition, our results align with prior work, indicating that chart type may also affect causal inference. Using data from the studies, we develop a model to capture the interaction between causal priors and visualized associations as they combine to impact a user's perceived causal relations. In addition to reporting the study results and analyses, we provide an open dataset of causal priors for 56 specific concept pairs that can serve as a potential benchmark for future studies. We also suggest remaining challenges and heuristic-based guidelines to help designers improve visualization design choices to better support visual causal inference.
AbstractList	“Correlation does not imply causation” is a famous mantra in statistical and visual analysis. However, consumers of visualizations often draw causal conclusions when only correlations between variables are shown. In this paper, we investigate factors that contribute to causal relationships users perceive in visualizations. We collected a corpus of concept pairs from variables in widely used datasets and created visualizations that depict varying correlative associations using three typical statistical chart types. We conducted two MTurk studies on (1) preconceived notions on causal relations without charts, and (2) perceived causal relations with charts, for each concept pair. Our results indicate that people make assumptions about causal relationships between pairs of concepts even without seeing any visualized data. Moreover, our results suggest that these assumptions constitute causal priors that, in combination with visualized association, impact how data visualizations are interpreted. The results also suggest that causal priors may lead to over- or under-estimation in perceived causal relations in different circumstances, and that those priors can also impact users' confidence in their causal assessments. In addition, our results align with prior work, indicating that chart type may also affect causal inference. Using data from the studies, we develop a model to capture the interaction between causal priors and visualized associations as they combine to impact a user's perceived causal relations. In addition to reporting the study results and analyses, we provide an open dataset of causal priors for 56 specific concept pairs that can serve as a potential benchmark for future studies. We also suggest remaining challenges and heuristic-based guidelines to help designers improve visualization design choices to better support visual causal inference. "Correlation does not imply causation" is a famous mantra in statistical and visual analysis. However, consumers of visualizations often draw causal conclusions when only correlations between variables are shown. In this paper, we investigate factors that contribute to causal relationships users perceive in visualizations. We collected a corpus of concept pairs from variables in widely used datasets and created visualizations that depict varying correlative associations using three typical statistical chart types. We conducted two MTurk studies on (1) preconceived notions on causal relations without charts, and (2) perceived causal relations with charts, for each concept pair. Our results indicate that people make assumptions about causal relationships between pairs of concepts even without seeing any visualized data. Moreover, our results suggest that these assumptions constitute causal priors that, in combination with visualized association, impact how data visualizations are interpreted. The results also suggest that causal priors may lead to over- or under-estimation in perceived causal relations in different circumstances, and that those priors can also impact users' confidence in their causal assessments. In addition, our results align with prior work, indicating that chart type may also affect causal inference. Using data from the studies, we develop a model to capture the interaction between causal priors and visualized associations as they combine to impact a user's perceived causal relations. In addition to reporting the study results and analyses, we provide an open dataset of causal priors for 56 specific concept pairs that can serve as a potential benchmark for future studies. We also suggest remaining challenges and heuristic-based guidelines to help designers improve visualization design choices to better support visual causal inference."Correlation does not imply causation" is a famous mantra in statistical and visual analysis. However, consumers of visualizations often draw causal conclusions when only correlations between variables are shown. In this paper, we investigate factors that contribute to causal relationships users perceive in visualizations. We collected a corpus of concept pairs from variables in widely used datasets and created visualizations that depict varying correlative associations using three typical statistical chart types. We conducted two MTurk studies on (1) preconceived notions on causal relations without charts, and (2) perceived causal relations with charts, for each concept pair. Our results indicate that people make assumptions about causal relationships between pairs of concepts even without seeing any visualized data. Moreover, our results suggest that these assumptions constitute causal priors that, in combination with visualized association, impact how data visualizations are interpreted. The results also suggest that causal priors may lead to over- or under-estimation in perceived causal relations in different circumstances, and that those priors can also impact users' confidence in their causal assessments. In addition, our results align with prior work, indicating that chart type may also affect causal inference. Using data from the studies, we develop a model to capture the interaction between causal priors and visualized associations as they combine to impact a user's perceived causal relations. In addition to reporting the study results and analyses, we provide an open dataset of causal priors for 56 specific concept pairs that can serve as a potential benchmark for future studies. We also suggest remaining challenges and heuristic-based guidelines to help designers improve visualization design choices to better support visual causal inference.
Author	Gotz, David Wang, Arran Zeyu Peck, Tabitha C. Wang, Wenyuan Borland, David
Author_xml	– sequence: 1 givenname: Arran Zeyu orcidid: 0000-0002-7491-7570 surname: Wang fullname: Wang, Arran Zeyu email: zeyuwang@cs.unc.edu organization: University of North Carolina at Chapel Hill (UNC), USA – sequence: 2 givenname: David orcidid: 0000-0002-0162-4080 surname: Borland fullname: Borland, David email: borland@renci.org organization: RENCI at UNC, USA – sequence: 3 givenname: Tabitha C. orcidid: 0000-0002-3667-7713 surname: Peck fullname: Peck, Tabitha C. email: tapeck@davidson.edu organization: Davidson College, USA – sequence: 4 givenname: Wenyuan orcidid: 0000-0001-8765-6675 surname: Wang fullname: Wang, Wenyuan email: vaapad@live.unc.edu organization: University of North Carolina at Chapel Hill (UNC), USA – sequence: 5 givenname: David orcidid: 0000-0002-6424-7374 surname: Gotz fullname: Gotz, David email: gotz@unc.edu organization: University of North Carolina at Chapel Hill (UNC), USA
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/39255145$$D View this record in MEDLINE/PubMed
BookMark	eNp9kE1LwzAYgINM3If-AEEkRy-d-WraHGXqnAzmYe4asvaNRrp0Ju1h_no7NkE8eEoCz_Pm5Rminq89IHRJyZhSom6Xq8l0zAgTYy5SyXN6ggZUCZqQlMhedydZljDJZB8NY_wghAqRqzPU54qlKRXpAC0mpo2mwi_B1SFi40u8fAcX8MzbqgVfAK49fm7LN9iAbyKuLT4ortlh5_HKxbZ7fEGJ701jztGpNVWEi-M5Qq-PD8vJUzJfTGeTu3lSdH83iaJUZoxaxVipOOQclAClrLXd_oIbkqeCS8JtCdymdg1sLWkBkpRlXhTU8hG6OczdhvqzhdjojYsFVJXxULdRc0pYnmWSiA69PqLtegOl3ga3MWGnfyJ0QHYAilDHGMDqwjWmcbVvgnGVpkTvc-t9br3PrY-5O5P-MX-G_-dcHRwHAL94mXW7cv4NgMCJwg
CODEN	ITVGEA
CitedBy_id	crossref_primary_10_1109_TVCG_2024_3496789 crossref_primary_10_1109_TVCG_2024_3456369
Cites_doi	10.1109/TVCG.2006.163 10.1111/j.1551-6708.1987.tb00863.x 10.1109/TVCG.2019.2934399 10.1109/mcg.2023.3338788 10.1109/TVCG.2015.2467671 10.1038/s42256-020-0197-y 10.1109/TVCG.2017.2744359 10.1017/CBO9780511803161 10.1145/2883851.2883904 10.1145/3290605.3300474 10.3758/s13423-016-1174-7 10.1145/3544548.3581236 10.1162/99608f92.3ab8a587 10.4324/9781315009292 10.1007/978-3-031-34738-2 10.1109/TVCG.2021.3114824 10.1007/978-3-319-26633-6_13 10.1111/cgf.13678 10.1111/j.1467-8659.2009.01694.x 10.1093/ije/dyh299 10.1109/TVCG.2012.196 10.3758/s13428-010-0023-2 10.1177/14738716241265120 10.7551/mitpress/1754.001.0001 10.1109/TVCG.2014.2346574 10.1109/TVCG.2021.3102051 10.1109/TVCG.2018.2865266 10.1109/beliv51497.2020.00010 10.1177/15291006211051956 10.1145/1502650.1502695 10.1109/TVCG.2020.3028984 10.1109/TVCG.2021.3114805 10.1186/s41235-018-0120-9 10.1177/14738716241229437 10.1145/3613904.3642813 10.1109/TVCG.2022.3207929 10.1201/9781315370279 10.1109/TVCG.2022.3209405 10.1109/TVCG.2018.2865147 10.1109/tvcg.2015.2467931 10.1057/ivs.2008.31 10.1145/3411764.3445674 10.1109/TVCG.2021.3114779 10.1109/TVCG.2017.2787113 10.1037/0033-2909.121.2.192 10.1109/TVCG.2021.3114875 10.3758/BRM.40.3.760 10.1145/3637298 10.3115/v1/W14-0707 10.1111/cgf.13680 10.1080/01621459.1988.10478598 10.1109/TVCG.2022.3209484 10.1016/S0020-7373(86)80019-0 10.1016/j.visinf.2024.06.002 10.1109/TVCG.2014.2346979 10.1109/TVCG.2010.177 10.1016/j.cogpsych.2005.05.004 10.1109/TVCG.2020.3030465 10.1145/3544548.3581524 10.1109/TVCG.2021.3098240 10.1109/TVCG.2010.164
ContentType	Journal Article
DBID	97E RIA RIE AAYXX CITATION NPM 7X8
DOI	10.1109/TVCG.2024.3456381
DatabaseName	IEEE Xplore (IEEE) IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef PubMed MEDLINE - Academic
DatabaseTitle	CrossRef PubMed MEDLINE - Academic
DatabaseTitleList	MEDLINE - Academic PubMed
Database_xml	– sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	1941-0506
EndPage	775
ExternalDocumentID	39255145 10_1109_TVCG_2024_3456381 10670433
Genre	orig-research Journal Article
GrantInformation_xml	– fundername: National Science Foundation grantid: 2211845 funderid: 10.13039/100000001
GroupedDBID	--- -~X .DC 0R~ 29I 4.4 53G 5GY 5VS 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACIWK AENEX AETIX AGQYO AGSQL AHBIQ AI. AIBXA AKJIK AKQYR ALLEH ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS EJD F5P HZ~ H~9 IEDLZ IFIPE IFJZH IPLJI JAVBF LAI M43 O9- OCL P2P PQQKQ RIA RIE RNI RNS RZB TN5 VH1 AAYXX CITATION AAYOK NPM RIG 7X8
ID	FETCH-LOGICAL-c392t-9116721f922d93e83e94e99fff19443a08543603fde3f5fbe2b61ce60dd8cc1f3
IEDL.DBID	RIE
ISICitedReferencesCount	1
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001367808800004&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1077-2626 1941-0506
IngestDate	Sun Sep 28 09:39:52 EDT 2025 Wed Mar 05 02:44:39 EST 2025 Sat Nov 29 03:31:50 EST 2025 Tue Nov 18 20:58:11 EST 2025 Wed Aug 27 03:03:27 EDT 2025
IsPeerReviewed	true
IsScholarly	true
Issue	1
Language	English
License	https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c392t-9116721f922d93e83e94e99fff19443a08543603fde3f5fbe2b61ce60dd8cc1f3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ORCID	0000-0002-6424-7374 0000-0002-0162-4080 0000-0002-3667-7713 0000-0001-8765-6675 0000-0002-7491-7570
PMID	39255145
PQID	3102877604
PQPubID	23479
PageCount	11
ParticipantIDs	pubmed_primary_39255145 crossref_citationtrail_10_1109_TVCG_2024_3456381 proquest_miscellaneous_3102877604 crossref_primary_10_1109_TVCG_2024_3456381 ieee_primary_10670433
PublicationCentury	2000
PublicationDate	2025-01-01
PublicationDateYYYYMMDD	2025-01-01
PublicationDate_xml	– month: 01 year: 2025 text: 2025-01-01 day: 01
PublicationDecade	2020
PublicationPlace	United States
PublicationPlace_xml	– name: United States
PublicationTitle	IEEE transactions on visualization and computer graphics
PublicationTitleAbbrev	TVCG
PublicationTitleAlternate	IEEE Trans Vis Comput Graph
PublicationYear	2025
Publisher	IEEE
Publisher_xml	– name: IEEE
References	ref13 ref57 ref12 ref56 ref15 ref59 ref14 ref58 ref11 ref55 ref10 ref54 ref17 ref16 ref19 ref18 ref50 ref46 ref45 ref48 ref47 ref42 ref41 ref44 ref43 ref49 ref8 ref7 ref9 ref4 ref3 ref6 ref5 ref40 Vigen (ref52) 2015 ref35 ref34 ref37 ref36 ref31 ref30 ref33 ref32 ref2 ref39 ref38 Tukey (ref51) 1977; 2 ref24 ref23 ref26 ref25 ref20 ref64 ref63 ref22 ref21 ref65 ref28 ref27 ref29 Wang (ref53) 2023 ref60 Asuncion (ref1) 2007 ref62 ref61
References_xml	– year: 2007 ident: ref1 publication-title: Uci machine learning repository – year: 2023 ident: ref53 article-title: Countering simpsons paradox with counterfactuals publication-title: IEEE VIS Posters – ident: ref23 doi: 10.1109/TVCG.2006.163 – ident: ref32 doi: 10.1111/j.1551-6708.1987.tb00863.x – ident: ref62 doi: 10.1109/TVCG.2019.2934399 – ident: ref5 doi: 10.1109/mcg.2023.3338788 – ident: ref29 doi: 10.1109/TVCG.2015.2467671 – ident: ref37 doi: 10.1038/s42256-020-0197-y – ident: ref48 doi: 10.1109/TVCG.2017.2744359 – ident: ref35 doi: 10.1017/CBO9780511803161 – ident: ref4 doi: 10.1145/2883851.2883904 – ident: ref36 doi: 10.1145/3290605.3300474 – ident: ref40 doi: 10.3758/s13423-016-1174-7 – ident: ref20 doi: 10.1145/3544548.3581236 – ident: ref25 doi: 10.1162/99608f92.3ab8a587 – ident: ref31 doi: 10.4324/9781315009292 – ident: ref49 doi: 10.1007/978-3-031-34738-2 – ident: ref27 doi: 10.1109/TVCG.2021.3114824 – ident: ref11 doi: 10.1007/978-3-319-26633-6_13 – ident: ref3 doi: 10.1111/cgf.13678 – ident: ref41 doi: 10.1111/j.1467-8659.2009.01694.x – ident: ref2 doi: 10.1093/ije/dyh299 – ident: ref50 doi: 10.1109/TVCG.2012.196 – ident: ref12 doi: 10.3758/s13428-010-0023-2 – ident: ref55 doi: 10.1177/14738716241265120 – ident: ref46 doi: 10.7551/mitpress/1754.001.0001 – ident: ref47 doi: 10.1109/TVCG.2014.2346574 – ident: ref24 doi: 10.1109/TVCG.2021.3102051 – year: 2015 ident: ref52 publication-title: Spurious correlations – ident: ref59 doi: 10.1109/TVCG.2018.2865266 – ident: ref7 doi: 10.1109/beliv51497.2020.00010 – ident: ref13 doi: 10.1177/15291006211051956 – ident: ref16 doi: 10.1145/1502650.1502695 – ident: ref30 doi: 10.1109/TVCG.2020.3028984 – ident: ref21 doi: 10.1109/TVCG.2021.3114805 – ident: ref34 doi: 10.1186/s41235-018-0120-9 – ident: ref54 doi: 10.1177/14738716241229437 – ident: ref39 doi: 10.1145/3613904.3642813 – ident: ref58 doi: 10.1109/TVCG.2022.3207929 – ident: ref61 doi: 10.1201/9781315370279 – ident: ref63 doi: 10.1109/TVCG.2022.3209405 – ident: ref45 doi: 10.1109/TVCG.2018.2865147 – ident: ref57 doi: 10.1109/tvcg.2015.2467931 – ident: ref17 doi: 10.1057/ivs.2008.31 – ident: ref65 doi: 10.1145/3411764.3445674 – ident: ref28 doi: 10.1109/TVCG.2021.3114779 – ident: ref60 doi: 10.1109/TVCG.2017.2787113 – ident: ref43 doi: 10.1037/0033-2909.121.2.192 – ident: ref10 doi: 10.1109/TVCG.2021.3114875 – ident: ref15 doi: 10.3758/BRM.40.3.760 – ident: ref44 doi: 10.1145/3637298 – ident: ref42 doi: 10.3115/v1/W14-0707 – ident: ref64 doi: 10.1111/cgf.13680 – ident: ref8 doi: 10.1080/01621459.1988.10478598 – ident: ref14 doi: 10.1109/TVCG.2022.3209484 – ident: ref9 doi: 10.1016/S0020-7373(86)80019-0 – volume: 2 volume-title: Exploratory Data Analysis year: 1977 ident: ref51 – ident: ref56 doi: 10.1016/j.visinf.2024.06.002 – ident: ref22 doi: 10.1109/TVCG.2014.2346979 – ident: ref33 doi: 10.1109/TVCG.2010.177 – ident: ref19 doi: 10.1016/j.cogpsych.2005.05.004 – ident: ref26 doi: 10.1109/TVCG.2020.3030465 – ident: ref6 doi: 10.1145/3544548.3581524 – ident: ref38 doi: 10.1109/TVCG.2021.3098240 – ident: ref18 doi: 10.1109/TVCG.2010.164
SSID	ssj0014489
Score	2.4610174
Snippet	“Correlation does not imply causation” is a famous mantra in statistical and visual analysis. However, consumers of visualizations often draw causal... "Correlation does not imply causation" is a famous mantra in statistical and visual analysis. However, consumers of visualizations often draw causal...
SourceID	proquest pubmed crossref ieee
SourceType	Aggregation Database Index Database Enrichment Source Publisher
StartPage	765
SubjectTerms	Association Bars Causal inference Causal prior Causality Cause effect analysis Cognition Correlation Data visualization Guidelines Perception and cognition Visualization
Title	Causal Priors and Their Influence on Judgements of Causality in Visualized Data
URI	https://ieeexplore.ieee.org/document/10670433 https://www.ncbi.nlm.nih.gov/pubmed/39255145 https://www.proquest.com/docview/3102877604
Volume	31
WOSCitedRecordID	wos001367808800004&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 1941-0506 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0014489 issn: 1077-2626 databaseCode: RIE dateStart: 19950101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LS8NAEB5sEdGD70d9sYInITXNbpPsUeobqR6q9BbS3VkoSCJN48Ff7-wmLfWg4C2HmZDMTDLf7LwAzmNDgU8YaS-WhntCm8CTfOR7KAyBex8DmbqR-U9Rvx8Ph_KlblZ3vTCI6IrPsG0vXS5f56q0R2WXdtyZHbjVgEYUhVWz1jxlQHGGrAoMIy8gmF6nMDu-vBy89e4oFAxEmxNeIB-1CiuECyxY6P7wR27Byu9Y0_mc241_Pu0mrNfgkl1V1rAFS5htw9rCyMEdeO6lZUE0L5NxPilYmmk2sLkC9jDbVsLyjD2WuqqJKVhuWMVCcJ2NM_Y2Lmwf5hdqdp1O0114vb0Z9O69eqmCp-iVp_bnFlLUZ2QQaMkx5igFSmmM6UgheEoQTPDQ50YjN10zwmAUdhSGvtaxUh3D96CZ5RkeAOORInxphCaALroxSoqllJbEFxkVqqgF_ky0iaonjtvFF--Jizx8mVjFJFYxSa2YFlzMWT6qcRt_Ee9aqS8QVgJvwdlMgQl9LDYDkmaYl0XCLZwi0_FFC_Yrzc65ZwZx-Mtdj2A1sLt_3fHLMTSnkxJPYFl9TsfF5JQschifOov8BgEe2Ho
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LT9tAEB5RQC0cKG1pCVC6lXqq5LDZ3djeIwrvhsAhRdwsZ3dWioRsFMcc-us7u3YiegCpNx9mLHtm7Plm5wXwI3UU-MSJjVLtZKSsE5GWEx6hcgTuOQqdh5H5w2Q0Su_v9W3brB56YRAxFJ9h11-GXL4tTe2Pyo78uDM_cOsNrPWVErxp11omDSjS0E2JYRIJAuptErPH9dH4bnBOwaBQXUmIgbzUBrwlZODhQv8fjxRWrLyMNoPXOXv_n8-7DVstvGTHjT18gBUsPsLms6GDn-BmkNcV0dzOpuWsYnlh2dhnC9jlYl8JKwt2VdumKqZipWMNCwF2Ni3Y3bTynZh_0LKTfJ7vwO-z0_HgImrXKkSGXnnuf28xxX1OC2G1xFSiVqi1c66nlZI5gTAlYy6dRen6boJiEvcMxtza1Jiek59htSgL3AUmE0MI0ylLEF31U9QUTRmriS9xJjZJB_hCtJlpZ4771RcPWYg9uM68YjKvmKxVTAd-Llkem4EbrxHveKk_I2wE3oHvCwVm9Ln4HEheYFlXmfSAKklirjrwpdHsknthEHsv3PUbvLsYXw-z4eXo1z5sCL8JOBzGHMDqfFbjV1g3T_NpNTsMdvkXVCfa2Q
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Causal+Priors+and+Their+Influence+on+Judgements+of+Causality+in+Visualized+Data&rft.jtitle=IEEE+transactions+on+visualization+and+computer+graphics&rft.au=Wang%2C+Arran+Zeyu&rft.au=Borland%2C+David&rft.au=Peck%2C+Tabitha+C&rft.au=Wang%2C+Wenyuan&rft.date=2025-01-01&rft.eissn=1941-0506&rft.volume=31&rft.issue=1&rft.spage=765&rft_id=info:doi/10.1109%2FTVCG.2024.3456381&rft_id=info%3Apmid%2F39255145&rft.externalDocID=39255145
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1077-2626&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1077-2626&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1077-2626&client=summon