{"id":{"repo_id":"gmu","oai_identifier":"oai:MARS:1920/10169"},"canonical_url":"https://search.dev.ndltd.org/etd/gmu/oai:MARS:1920/10169","repository":{"repo_id":"gmu","name":"George Mason University","base_url":"https://mars.gmu.edu/server/oai/request"},"display":{"title":"Exploratory Causal Analysis in Bivariate Time Series Data","abstract":"Many scientific disciplines rely on observational data of systems for which it is difficult (or impossible) to implement controlled experiments and data analysis techniques are required for identifying causal information and relationships directly from observational data. This need has lead to the development of many different time series causality approaches and tools including transfer entropy, convergent cross-mapping (CCM), and Granger causality statistics.","abstract_html":"Many scientific disciplines rely on observational data of systems for which it is difficult (or impossible) to implement controlled experiments and data analysis techniques are required for identifying causal information and relationships directly from observational data. This need has lead to the development of many different time series causality approaches and tools including transfer entropy, convergent cross-mapping (CCM), and Granger causality statistics.","abstract_has_math":false,"creators":["McCracken, James M."],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015","date_published":"2015","updated_at":"2026-07-27T19:52:12Z","subjects":["Physics","Causality","Granger","Leaning","Pairwise asymmetric inference","Penchant","Time series analysis"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["hdl:1920/10169"],"render_values":[{"text":"hdl:1920/10169","href":null,"code":true}]}]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2015"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Physics","Causality","Granger","Leaning","Pairwise asymmetric inference","Penchant","Time series analysis"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["hdl:1920/10169"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.other","label":"Dc Description Other","values":["Many scientific disciplines rely on observational data of systems for which it is difficult (or impossible) to implement controlled experiments and data analysis techniques are required for identifying causal information and relationships directly from observational data. This need has lead to the development of many different time series causality approaches and tools including transfer entropy, convergent cross-mapping (CCM), and Granger causality statistics."]},{"key":"dc:title","label":"Title","values":["Exploratory Causal Analysis in Bivariate Time Series Data"]}]}],"canonical_facts":{"dc:date.issued":["2015"],"dc:description.other":["Many scientific disciplines rely on observational data of systems for which it is difficult (or impossible) to implement controlled experiments and data analysis techniques are required for identifying causal information and relationships directly from observational data. This need has lead to the development of many different time series causality approaches and tools including transfer entropy, convergent cross-mapping (CCM), and Granger causality statistics."],"dc:identifier":["hdl:1920/10169"],"dc:subject":["Physics","Causality","Granger","Leaning","Pairwise asymmetric inference","Penchant","Time series analysis"],"dc:title":["Exploratory Causal Analysis in Bivariate Time Series Data"],"dc:type":["Dissertation"]},"updated_at":"2026-07-27T19:52:12Z"}