{"id":"https://openalex.org/W4407953577","doi":"https://doi.org/10.1145/3701551.3703525","title":"Graph Disentangle Causal Model: Enhancing Causal Inference in Networked Observational Data","display_name":"Graph Disentangle Causal Model: Enhancing Causal Inference in Networked Observational Data","publication_year":2025,"publication_date":"2025-02-26","ids":{"openalex":"https://openalex.org/W4407953577","doi":"https://doi.org/10.1145/3701551.3703525"},"language":"en","primary_location":{"id":"doi:10.1145/3701551.3703525","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3701551.3703525","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Eighteenth ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3701551.3703525","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5076909486","display_name":"Binbin Hu","orcid":"https://orcid.org/0000-0002-2505-1619"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Binbin Hu","raw_affiliation_strings":["Ant Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Ant Group, Hangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055533193","display_name":"Zhicheng An","orcid":"https://orcid.org/0000-0002-5344-8509"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhicheng An","raw_affiliation_strings":["Ant Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Ant Group, Hangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062608506","display_name":"Zhengwei Wu","orcid":"https://orcid.org/0000-0002-9695-3863"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhengwei Wu","raw_affiliation_strings":["Ant Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Ant Group, Hangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062499743","display_name":"Ke Tu","orcid":"https://orcid.org/0009-0009-4922-1684"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ke Tu","raw_affiliation_strings":["Ant Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Ant Group, Hangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114860377","display_name":"Ziqi Liu","orcid":"https://orcid.org/0000-0002-4112-3504"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ziqi Liu","raw_affiliation_strings":["Ant Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Ant Group, Hangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032099283","display_name":"Zhiqiang Zhang","orcid":"https://orcid.org/0000-0002-2321-7259"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhiqiang Zhang","raw_affiliation_strings":["Ant Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Ant Group, Hangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045140292","display_name":"Jun Zhou","orcid":"https://orcid.org/0000-0001-6033-6102"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jun Zhou","raw_affiliation_strings":["Ant Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Ant Group, Hangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037756030","display_name":"Yufei Feng","orcid":"https://orcid.org/0000-0002-1144-7823"},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yufei Feng","raw_affiliation_strings":["Alibaba, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Alibaba, Hangzhou, China","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100362815","display_name":"Jiawei Chen","orcid":"https://orcid.org/0000-0002-4752-2629"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiawei Chen","raw_affiliation_strings":["Zhejiang University, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":9,"corresponding_author_ids":["https://openalex.org/A5076909486"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.8473,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.86875669,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10845","display_name":"Advanced Causal Inference Techniques","score":0.9979000091552734,"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"}},"topics":[{"id":"https://openalex.org/T10845","display_name":"Advanced Causal Inference Techniques","score":0.9979000091552734,"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"}},{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9901999831199646,"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"}},{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.987500011920929,"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/causal-inference","display_name":"Causal inference","score":0.8231137990951538},{"id":"https://openalex.org/keywords/observational-study","display_name":"Observational study","score":0.7822321653366089},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.614754319190979},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6005663275718689},{"id":"https://openalex.org/keywords/causal-model","display_name":"Causal model","score":0.5676075220108032},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5119253396987915},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4335455596446991},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.32703664898872375},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.31869447231292725},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2938380539417267},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.2929031550884247},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.23307061195373535},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.21089211106300354}],"concepts":[{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.8231137990951538},{"id":"https://openalex.org/C23131810","wikidata":"https://www.wikidata.org/wiki/Q818574","display_name":"Observational study","level":2,"score":0.7822321653366089},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.614754319190979},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6005663275718689},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.5676075220108032},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5119253396987915},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4335455596446991},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32703664898872375},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.31869447231292725},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2938380539417267},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.2929031550884247},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.23307061195373535},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.21089211106300354},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3701551.3703525","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3701551.3703525","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Eighteenth ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3701551.3703525","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3701551.3703525","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Eighteenth ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.5600000023841858,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W2064903582","https://openalex.org/W2150291618","https://openalex.org/W2187089797","https://openalex.org/W2208550830","https://openalex.org/W2560674852","https://openalex.org/W2624816748","https://openalex.org/W2965237851","https://openalex.org/W2996910665","https://openalex.org/W3122193054","https://openalex.org/W3150893739","https://openalex.org/W3157999218","https://openalex.org/W3171375092","https://openalex.org/W3171442082","https://openalex.org/W3211276971","https://openalex.org/W4249440929","https://openalex.org/W4306317407","https://openalex.org/W4367047038","https://openalex.org/W4384887426","https://openalex.org/W4396757523","https://openalex.org/W4401863428"],"related_works":["https://openalex.org/W2979832559","https://openalex.org/W3128129045","https://openalex.org/W4324300609","https://openalex.org/W4387531643","https://openalex.org/W4385077270","https://openalex.org/W4396768342","https://openalex.org/W4231150422","https://openalex.org/W4400520609","https://openalex.org/W3164869265","https://openalex.org/W2997970376"],"abstract_inverted_index":{"Estimating":[0],"individual":[1,33],"treatment":[2],"effects":[3],"(ITE)":[4],"from":[5],"observational":[6],"data":[7],"is":[8,185,202],"a":[9,126,146,162,181],"critical":[10],"task":[11],"across":[12],"various":[13],"domains.":[14],"However,":[15],"many":[16],"existing":[17],"works":[18],"on":[19,108,208],"ITE":[20,138],"estimation":[21,139],"overlook":[22],"the":[23,32,52,61,67,73,94,100,109,115,130,141,189],"influence":[24],"of":[25,63,75,117,167,198],"hidden":[26,53],"confounders,":[27],"which":[28],"remain":[29],"unobserved":[30],"at":[31],"unit":[34,152],"level.":[35],"To":[36,120],"address":[37],"this":[38,122],"limitation,":[39],"researchers":[40],"have":[41],"utilized":[42],"graph":[43,163,170],"neural":[44],"networks":[45],"to":[46,50,136,150,172,187],"aggregate":[47],"neighbors'":[48],"features":[49,83,153],"capture":[51],"confounders":[54,85],"and":[55,69,86,96,102,104,156,176],"mitigate":[56,121],"confounding":[57],"bias":[58],"by":[59,204],"minimizing":[60],"discrepancy":[62],"confounder":[64,103,110,157,178],"representations":[65,111,191],"between":[66,93],"treated":[68,95],"control":[70,97],"groups.":[71,98],"Despite":[72],"success":[74],"these":[76],"approaches,":[77],"practical":[78],"scenarios":[79],"often":[80],"treat":[81],"all":[82],"as":[84,192],"involve":[87],"substantial":[88],"differences":[89],"in":[90,140],"feature":[91],"distributions":[92],"Confusing":[99],"adjustment":[101,155],"enforcing":[105],"strict":[106],"balance":[107],"could":[112],"potentially":[113],"undermine":[114],"effectiveness":[116,197],"outcome":[118],"prediction.":[119],"issue,":[123],"we":[124,160],"propose":[125],"novel":[127],"framework":[128],"called":[129],"Graph":[131],"Disentangle":[132],"Causal":[133],"model":[134],"(GDC)":[135],"conduct":[137],"network":[142],"setting.":[143],"GDC":[144],"utilizes":[145],"causal":[147,182,194],"disentangle":[148],"module":[149,165,184],"separate":[151],"into":[154],"representations.":[158,179],"Then":[159],"design":[161],"aggregation":[164],"consisting":[166],"three":[168],"distinct":[169],"aggregators":[171],"obtain":[173],"adjustment,":[174],"confounder,":[175],"counterfactual":[177],"Finally,":[180],"constraint":[183],"employed":[186],"enforce":[188],"disentangled":[190],"true":[193],"factors.":[195],"The":[196],"our":[199],"proposed":[200],"method":[201],"demonstrated":[203],"conducting":[205],"comprehensive":[206],"experiments":[207],"two":[209],"networked":[210],"datasets.":[211]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2025-12-23T23:11:35.936235","created_date":"2025-10-10T00:00:00"}
