{"id":"https://openalex.org/W4240782786","doi":"https://doi.org/10.7287/peerj.preprints.27416v1","title":"Data based intervention approach for Complexity-Causality measure","display_name":"Data based intervention approach for Complexity-Causality measure","publication_year":2018,"publication_date":"2018-12-07","ids":{"openalex":"https://openalex.org/W4240782786","doi":"https://doi.org/10.7287/peerj.preprints.27416v1"},"language":"en","primary_location":{"id":"doi:10.7287/peerj.preprints.27416v1","is_oa":true,"landing_page_url":"https://doi.org/10.7287/peerj.preprints.27416v1","pdf_url":"https://peerj.com/preprints/27416.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"},"type":"preprint","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://peerj.com/preprints/27416.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5090695332","display_name":"Aditi Kathpalia","orcid":"https://orcid.org/0000-0002-8349-7301"},"institutions":[{"id":"https://openalex.org/I22956069","display_name":"National Institute of Advanced Studies","ror":"https://ror.org/012wm5r19","country_code":"IN","type":"education","lineage":["https://openalex.org/I22956069"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Aditi Kathpalia","raw_affiliation_strings":["National Institute of Advanced Studies, Bengaluru, Karnataka, India"],"affiliations":[{"raw_affiliation_string":"National Institute of Advanced Studies, Bengaluru, Karnataka, India","institution_ids":["https://openalex.org/I22956069"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5077181669","display_name":"Nithin Nagaraj","orcid":"https://orcid.org/0000-0003-0097-4131"},"institutions":[{"id":"https://openalex.org/I22956069","display_name":"National Institute of Advanced Studies","ror":"https://ror.org/012wm5r19","country_code":"IN","type":"education","lineage":["https://openalex.org/I22956069"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Nithin Nagaraj","raw_affiliation_strings":["National Institute of Advanced Studies, Bengaluru, Karnataka, India"],"affiliations":[{"raw_affiliation_string":"National Institute of Advanced Studies, Bengaluru, Karnataka, India","institution_ids":["https://openalex.org/I22956069"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5090695332"],"corresponding_institution_ids":["https://openalex.org/I22956069"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.34416313,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9779000282287598,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9779000282287598,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.97079998254776,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9610999822616577,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/causality","display_name":"Causality (physics)","score":0.764262318611145},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6162477135658264},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.5970075726509094},{"id":"https://openalex.org/keywords/transfer-entropy","display_name":"Transfer entropy","score":0.5883392691612244},{"id":"https://openalex.org/keywords/granger-causality","display_name":"Granger causality","score":0.5864631533622742},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.4566057026386261},{"id":"https://openalex.org/keywords/decimation","display_name":"Decimation","score":0.42932260036468506},{"id":"https://openalex.org/keywords/aliasing","display_name":"Aliasing","score":0.4290677011013031},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4278026223182678},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.38645654916763306},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3792281746864319},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.37299466133117676},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2993127703666687},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.28263163566589355},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.23418474197387695},{"id":"https://openalex.org/keywords/principle-of-maximum-entropy","display_name":"Principle of maximum entropy","score":0.11116212606430054},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.08190250396728516}],"concepts":[{"id":"https://openalex.org/C64357122","wikidata":"https://www.wikidata.org/wiki/Q1149766","display_name":"Causality (physics)","level":2,"score":0.764262318611145},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6162477135658264},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.5970075726509094},{"id":"https://openalex.org/C182049051","wikidata":"https://www.wikidata.org/wiki/Q17147155","display_name":"Transfer entropy","level":3,"score":0.5883392691612244},{"id":"https://openalex.org/C129824826","wikidata":"https://www.wikidata.org/wiki/Q2630107","display_name":"Granger causality","level":2,"score":0.5864631533622742},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4566057026386261},{"id":"https://openalex.org/C173642442","wikidata":"https://www.wikidata.org/wiki/Q1253346","display_name":"Decimation","level":3,"score":0.42932260036468506},{"id":"https://openalex.org/C4069607","wikidata":"https://www.wikidata.org/wiki/Q868732","display_name":"Aliasing","level":3,"score":0.4290677011013031},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4278026223182678},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38645654916763306},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3792281746864319},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37299466133117676},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2993127703666687},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.28263163566589355},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.23418474197387695},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.11116212606430054},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.08190250396728516},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C136536468","wikidata":"https://www.wikidata.org/wiki/Q1225894","display_name":"Undersampling","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.7287/peerj.preprints.27416v1","is_oa":true,"landing_page_url":"https://doi.org/10.7287/peerj.preprints.27416v1","pdf_url":"https://peerj.com/preprints/27416.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"}],"best_oa_location":{"id":"doi:10.7287/peerj.preprints.27416v1","is_oa":true,"landing_page_url":"https://doi.org/10.7287/peerj.preprints.27416v1","pdf_url":"https://peerj.com/preprints/27416.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4240782786.pdf","grobid_xml":"https://content.openalex.org/works/W4240782786.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2035792466","https://openalex.org/W1652270319","https://openalex.org/W2977645287","https://openalex.org/W4251418261","https://openalex.org/W1972675643","https://openalex.org/W4313343711","https://openalex.org/W2911392473","https://openalex.org/W2154758532","https://openalex.org/W2152123380","https://openalex.org/W1989349725"],"abstract_inverted_index":{"Causality":[0,142,164],"testing":[1,212],"methods":[2,13,51],"are":[3,17,89],"being":[4],"widely":[5],"used":[6],"in":[7,95,213],"various":[8],"disciplines":[9],"of":[10,36,43,46,74,130,135,158,175,194],"science.":[11],"Model-free":[12],"for":[14,210],"causality":[15,67,112,211],"estimation":[16],"very":[18],"useful":[19],"as":[20,162,206],"the":[21,25,41,71,96,127],"underlying":[22],"model":[23],"generating":[24],"data":[26],"is":[27,69,114,120,143,154,171],"often":[28,85],"unknown.":[29],"However,":[30],"existing":[31,159,201],"model-free":[32],"measures":[33,62,160],"assume":[34],"separability":[35],"cause":[37],"and":[38,48,76,87,148,151,165,180,192],"effect":[39],"at":[40],"level":[42],"individual":[44],"samples":[45],"measurements":[47],"unlike":[49],"model-based":[50],"do":[52],"not":[53,115],"perform":[54],"any":[55],"intervention":[56],"to":[57,110,173],"learn":[58],"causal":[59],"relationships.":[60],"These":[61],"can":[63],"thus":[64],"only":[65],"capture":[66,111],"which":[68,113],"by":[70],"associational":[72],"occurrence":[73],"\u2018cause\u2019":[75,86],"\u2018effect\u2019":[77,88],"between":[78],"well":[79],"separated":[80],"samples.":[81],"In":[82],"real-world":[83],"processes,":[84],"inherently":[90],"inseparable":[91,94],"or":[92],"become":[93],"acquired":[97],"measurements.":[98,136],"We":[99],"propose":[100],"a":[101],"novel":[102],"measure":[103,170,199],"that":[104,157],"uses":[105],"an":[106,207],"adaptive":[107],"interventional":[108],"scheme":[109,119],"merely":[116],"associational.":[117],"The":[118,137,168],"based":[121],"on":[122,132,146],"characterizing":[123],"complexities":[124],"associated":[125],"with":[126,156],"dynamical":[128],"evolution":[129],"processes":[131],"short":[133],"windows":[134],"formulated":[138],"measure,":[139],"Compression-":[140],"Complexity":[141],"rigorously":[144],"tested":[145],"simulated":[147],"real":[149,214],"datasets":[150],"its":[152],"performance":[153],"compared":[155],"such":[161],"Granger":[163],"Transfer":[166],"Entropy.":[167],"proposed":[169],"robust":[172],"presence":[174,193],"noise,":[176],"long-term":[177],"memory,":[178],"filtering":[179],"decimation,":[181],"low":[182],"temporal":[183],"resolution":[184],"(including":[185],"aliasing),":[186],"non-uniform":[187],"sampling,":[188],"finite":[189],"length":[190],"signals":[191],"common":[195],"driving":[196],"variables.":[197],"Our":[198],"outperforms":[200],"state-of-the-art":[202],"measures,":[203],"establishing":[204],"itself":[205],"effective":[208],"tool":[209],"world":[215],"applications.":[216]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}