{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/124696"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/124696","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"DoViz: Intervention-centric interactive visualization for causal inference","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-05-01","abstract_has_math":false,"creators":["Chandratre, Atharv Shripad"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Sundaram, Hari"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-05","date_published":"2024-05","updated_at":"2026-07-22T22:25:02Z","subjects":["Causal Inference","Interactive Visualization","Data Visualization Tools","Intervention Analysis","Comparative Scenario Analysis","User Interface Design","Human-computer Interaction","Causal Reasoning","Visualization Software"],"languages":["en","eng"],"rights":["Copyright 2024 Atharv Chandratre"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/124696","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Sundaram, Hari"]},{"key":"dc:creator","label":"Author","values":["Chandratre, Atharv Shripad"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-05","2024-04-29"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Causal Inference","Interactive Visualization","Data Visualization Tools","Intervention Analysis","Comparative Scenario Analysis","User Interface Design","Human-computer Interaction","Causal Reasoning","Visualization Software"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2024 Atharv Chandratre"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/124696"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-05-01","The student, Atharv Chandratre, accepted the attached license on 2024-04-23 at 17:07.","The student, Atharv Chandratre, submitted this Thesis for approval on 2024-04-23 at 17:16.","This Thesis was approved for publication on 2024-04-29 at 10:06.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20594 on 2024-09-16 at 00:50:20","Causal inference is the process of understanding and quantifying cause-and-effect relationships from observed data. The process of causal inference often requires analysts to use visualizations for evaluating accuracy. However, existing visualization tools often lack the ability to communicate the effect of performing interventions on the data and comparatively visualizing their results. In this thesis, we address this gap using DoViz, an intervention-centric interactive visualization prototype. DoViz enables users to interact with independent variables, simulate explicit interventions, condition on confounders, and visualize the resultant causal effects. The user interface comprises an Intervention Panel for performing intuitive manipulations and an Inference Panel for visualizing intervention outcomes. A key feature of DoViz is its ability to perform comparative causal scenario analysis. Users can juxtapose multiple scenarios side-by-side, helping them draw more informed conclusions. The design process was driven by an iterative needfinding study, distilling a user-centric workflow around intervention, analysis, and comparison."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["DoViz: Intervention-centric interactive visualization for causal inference"]}]}],"canonical_facts":{"dc:contributor":["Sundaram, Hari"],"dc:creator":["Chandratre, Atharv Shripad"],"dc:date":["2024-05","2024-04-29"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-05-01","The student, Atharv Chandratre, accepted the attached license on 2024-04-23 at 17:07.","The student, Atharv Chandratre, submitted this Thesis for approval on 2024-04-23 at 17:16.","This Thesis was approved for publication on 2024-04-29 at 10:06.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20594 on 2024-09-16 at 00:50:20","Causal inference is the process of understanding and quantifying cause-and-effect relationships from observed data. The process of causal inference often requires analysts to use visualizations for evaluating accuracy. However, existing visualization tools often lack the ability to communicate the effect of performing interventions on the data and comparatively visualizing their results. In this thesis, we address this gap using DoViz, an intervention-centric interactive visualization prototype. DoViz enables users to interact with independent variables, simulate explicit interventions, condition on confounders, and visualize the resultant causal effects. The user interface comprises an Intervention Panel for performing intuitive manipulations and an Inference Panel for visualizing intervention outcomes. A key feature of DoViz is its ability to perform comparative causal scenario analysis. Users can juxtapose multiple scenarios side-by-side, helping them draw more informed conclusions. The design process was driven by an iterative needfinding study, distilling a user-centric workflow around intervention, analysis, and comparison."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/124696"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Atharv Chandratre"],"dc:subject":["Causal Inference","Interactive Visualization","Data Visualization Tools","Intervention Analysis","Comparative Scenario Analysis","User Interface Design","Human-computer Interaction","Causal Reasoning","Visualization Software"],"dc:title":["DoViz: Intervention-centric interactive visualization for causal inference"],"dc:type":["text"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:02Z"}