{"id":"https://openalex.org/W2116993992","doi":"https://doi.org/10.1162/neco_a_00708","title":"Structural Intervention Distance for Evaluating Causal Graphs","display_name":"Structural Intervention Distance for Evaluating Causal Graphs","publication_year":2015,"publication_date":"2015-01-20","ids":{"openalex":"https://openalex.org/W2116993992","doi":"https://doi.org/10.1162/neco_a_00708","mag":"2116993992","pmid":"https://pubmed.ncbi.nlm.nih.gov/25602767"},"language":"en","primary_location":{"id":"doi:10.1162/neco_a_00708","is_oa":false,"landing_page_url":"https://doi.org/10.1162/neco_a_00708","pdf_url":null,"source":{"id":"https://openalex.org/S207023548","display_name":"Neural Computation","issn_l":"0899-7667","issn":["0899-7667","1530-888X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315718","host_organization_name":"The MIT Press","host_organization_lineage":["https://openalex.org/P4310315718"],"host_organization_lineage_names":["The MIT Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Computation","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5002127148","display_name":"Jonas Peters","orcid":"https://orcid.org/0000-0002-1487-7511"},"institutions":[{"id":"https://openalex.org/I35440088","display_name":"ETH Zurich","ror":"https://ror.org/05a28rw58","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I35440088"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Jonas Peters","raw_affiliation_strings":["Seminar for Statistics, Department of Mathematics, ETH Z\u00fcrich 8092, Switzerland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Seminar for Statistics, Department of Mathematics, ETH Z\u00fcrich 8092, Switzerland","institution_ids":["https://openalex.org/I35440088"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033240072","display_name":"Peter B\u00fchlmann","orcid":"https://orcid.org/0000-0002-1782-6015"},"institutions":[{"id":"https://openalex.org/I35440088","display_name":"ETH Zurich","ror":"https://ror.org/05a28rw58","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I35440088"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Peter B\u00fchlmann","raw_affiliation_strings":["Seminar for Statistics, Department of Mathematics, ETH Z\u00fcrich 8092, Switzerland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Seminar for Statistics, Department of Mathematics, ETH Z\u00fcrich 8092, Switzerland","institution_ids":["https://openalex.org/I35440088"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":6.6776,"has_fulltext":false,"cited_by_count":99,"citation_normalized_percentile":{"value":0.96731676,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"27","issue":"3","first_page":"771","last_page":"799"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9998999834060669,"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/T10845","display_name":"Advanced Causal Inference Techniques","score":0.9634000062942505,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/directed-acyclic-graph","display_name":"Directed acyclic graph","score":0.7919304370880127},{"id":"https://openalex.org/keywords/causal-inference","display_name":"Causal inference","score":0.653174877166748},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6162536144256592},{"id":"https://openalex.org/keywords/graphical-model","display_name":"Graphical model","score":0.5178349614143372},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.504428505897522},{"id":"https://openalex.org/keywords/closeness","display_name":"Closeness","score":0.4989650249481201},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.46282392740249634},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.43992263078689575},{"id":"https://openalex.org/keywords/hamming-distance","display_name":"Hamming distance","score":0.43932729959487915},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.4368695616722107},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.30589187145233154},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.268902063369751},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.24890807271003723},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.1073530912399292}],"concepts":[{"id":"https://openalex.org/C74197172","wikidata":"https://www.wikidata.org/wiki/Q1195339","display_name":"Directed acyclic graph","level":2,"score":0.7919304370880127},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.653174877166748},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6162536144256592},{"id":"https://openalex.org/C155846161","wikidata":"https://www.wikidata.org/wiki/Q1143367","display_name":"Graphical model","level":2,"score":0.5178349614143372},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.504428505897522},{"id":"https://openalex.org/C2779545769","wikidata":"https://www.wikidata.org/wiki/Q5135364","display_name":"Closeness","level":2,"score":0.4989650249481201},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.46282392740249634},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.43992263078689575},{"id":"https://openalex.org/C193319292","wikidata":"https://www.wikidata.org/wiki/Q272172","display_name":"Hamming distance","level":2,"score":0.43932729959487915},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.4368695616722107},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.30589187145233154},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.268902063369751},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.24890807271003723},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.1073530912399292},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1162/neco_a_00708","is_oa":false,"landing_page_url":"https://doi.org/10.1162/neco_a_00708","pdf_url":null,"source":{"id":"https://openalex.org/S207023548","display_name":"Neural Computation","issn_l":"0899-7667","issn":["0899-7667","1530-888X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315718","host_organization_name":"The MIT Press","host_organization_lineage":["https://openalex.org/P4310315718"],"host_organization_lineage_names":["The MIT Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Computation","raw_type":"journal-article"},{"id":"pmid:25602767","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/25602767","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural computation","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320320300","display_name":"European Commission","ror":"https://ror.org/00k4n6c32"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W4123081","https://openalex.org/W34433402","https://openalex.org/W175219726","https://openalex.org/W894628922","https://openalex.org/W1486066286","https://openalex.org/W1489999654","https://openalex.org/W1511986666","https://openalex.org/W1524326598","https://openalex.org/W1531368347","https://openalex.org/W1822300807","https://openalex.org/W1977446441","https://openalex.org/W1979740015","https://openalex.org/W1990930479","https://openalex.org/W2086331397","https://openalex.org/W2095690451","https://openalex.org/W2100094988","https://openalex.org/W2111061246","https://openalex.org/W2115984935","https://openalex.org/W2124758737","https://openalex.org/W2143891888","https://openalex.org/W2160951788","https://openalex.org/W2163687466","https://openalex.org/W2165190832","https://openalex.org/W2953077341","https://openalex.org/W2963429013","https://openalex.org/W3098710260","https://openalex.org/W3103539622","https://openalex.org/W4285719527","https://openalex.org/W4302423442","https://openalex.org/W4388323202"],"related_works":["https://openalex.org/W4220807758","https://openalex.org/W79495945","https://openalex.org/W1505105018","https://openalex.org/W2035724326","https://openalex.org/W2443765813","https://openalex.org/W4378718308","https://openalex.org/W4299933434","https://openalex.org/W17595654","https://openalex.org/W4302568932","https://openalex.org/W2962752536"],"abstract_inverted_index":{"Causal":[0],"inference":[1,22,65],"relies":[2],"on":[3,47,133],"the":[4,38,54,104,134],"structure":[5],"of":[6,61,82,120],"a":[7,10,34,48,113,125],"graph,":[8],"often":[9],"directed":[11],"acyclic":[12],"graph":[13],"(DAG).":[14],"Different":[15],"graphs":[16,74],"may":[17],"result":[18],"in":[19,59],"different":[20,25],"causal":[21,64],"statements":[23],"and":[24,52,110,123],"intervention":[26,40],"distributions.":[27],"To":[28],"quantify":[29],"such":[30],"differences,":[31],"we":[32],"propose":[33],"(pre-)metric":[35],"between":[36,56],"DAGs,":[37,83],"structural":[39,107],"distance":[41,109,122],"(SID).":[42],"The":[43,99],"SID":[44,100],"is":[45,68,85],"based":[46],"graphical":[49],"criterion":[50],"only":[51],"quantifies":[53],"closeness":[55],"two":[57],"DAGs":[58,93],"terms":[60],"their":[62],"corresponding":[63],"statements.":[66],"It":[67],"therefore":[69,111],"well":[70],"suited":[71],"for":[72,78],"evaluating":[73],"that":[75,94],"are":[76],"used":[77,106],"computing":[79],"interventions.":[80],"Instead":[81],"it":[84],"also":[86],"possible":[87],"to":[88],"compare":[89],"CPDAGs,":[90],"completed":[91],"partially":[92],"represent":[95],"Markov":[96],"equivalence":[97],"classes.":[98],"differs":[101],"significantly":[102],"from":[103],"widely":[105],"Hamming":[108],"constitutes":[112],"valuable":[114],"additional":[115],"measure.":[116],"We":[117],"discuss":[118],"properties":[119],"this":[121],"provide":[124],"(reasonably)":[126],"efficient":[127],"implementation":[128],"with":[129],"software":[130],"code":[131],"available":[132],"first":[135],"author's":[136],"home":[137],"page.":[138]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":20},{"year":2024,"cited_by_count":13},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":16},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":6},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":3},{"year":2014,"cited_by_count":3},{"year":2013,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}