{"id":"https://openalex.org/W4385568142","doi":"https://doi.org/10.1145/3580305.3599297","title":"Deception by Omission: Using Adversarial Missingness to Poison Causal Structure Learning","display_name":"Deception by Omission: Using Adversarial Missingness to Poison Causal Structure Learning","publication_year":2023,"publication_date":"2023-08-04","ids":{"openalex":"https://openalex.org/W4385568142","doi":"https://doi.org/10.1145/3580305.3599297"},"language":"en","primary_location":{"id":"doi:10.1145/3580305.3599297","is_oa":true,"landing_page_url":"http://dx.doi.org/10.1145/3580305.3599297","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3580305.3599297","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 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3580305.3599297","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5045572599","display_name":"Deniz Koyuncu","orcid":"https://orcid.org/0000-0001-6316-8853"},"institutions":[{"id":"https://openalex.org/I165799507","display_name":"Rensselaer Polytechnic Institute","ror":"https://ror.org/01rtyzb94","country_code":"US","type":"education","lineage":["https://openalex.org/I165799507"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Deniz Koyuncu","raw_affiliation_strings":["Rensselaer Polytechnic Institute, Troy, NY, USA"],"affiliations":[{"raw_affiliation_string":"Rensselaer Polytechnic Institute, Troy, NY, USA","institution_ids":["https://openalex.org/I165799507"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038646656","display_name":"Alex Gittens","orcid":"https://orcid.org/0000-0003-3482-0157"},"institutions":[{"id":"https://openalex.org/I165799507","display_name":"Rensselaer Polytechnic Institute","ror":"https://ror.org/01rtyzb94","country_code":"US","type":"education","lineage":["https://openalex.org/I165799507"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alex Gittens","raw_affiliation_strings":["Rensselaer Polytechnic Institute, Troy, NY, USA"],"affiliations":[{"raw_affiliation_string":"Rensselaer Polytechnic Institute, Troy, NY, USA","institution_ids":["https://openalex.org/I165799507"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073676361","display_name":"B\u00fclent Yener","orcid":"https://orcid.org/0000-0003-3989-6097"},"institutions":[{"id":"https://openalex.org/I165799507","display_name":"Rensselaer Polytechnic Institute","ror":"https://ror.org/01rtyzb94","country_code":"US","type":"education","lineage":["https://openalex.org/I165799507"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"B\u00fclent Yener","raw_affiliation_strings":["Rensselaer Polytechnic Institute, Troy, NY, USA"],"affiliations":[{"raw_affiliation_string":"Rensselaer Polytechnic Institute, Troy, NY, USA","institution_ids":["https://openalex.org/I165799507"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5030243906","display_name":"Moti Yung","orcid":"https://orcid.org/0000-0003-0848-0873"},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]},{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Moti Yung","raw_affiliation_strings":["Google LLC &amp; Columbia University, New York, NY, USA"],"affiliations":[{"raw_affiliation_string":"Google LLC &amp; Columbia University, New York, NY, USA","institution_ids":["https://openalex.org/I1291425158","https://openalex.org/I78577930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5045572599"],"corresponding_institution_ids":["https://openalex.org/I165799507"],"apc_list":null,"apc_paid":null,"fwci":0.1746,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.54522736,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"8","issue":null,"first_page":"1164","last_page":"1175"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9991999864578247,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9991999864578247,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9954000115394592,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9883999824523926,"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/computer-science","display_name":"Computer science","score":0.7301954030990601},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6999853253364563},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6793112754821777},{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.649238109588623},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.6129288673400879},{"id":"https://openalex.org/keywords/causal-inference","display_name":"Causal inference","score":0.5241742134094238},{"id":"https://openalex.org/keywords/adversarial-machine-learning","display_name":"Adversarial machine learning","score":0.5044182538986206},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.48500868678092957},{"id":"https://openalex.org/keywords/correctness","display_name":"Correctness","score":0.47336336970329285},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.45748773217201233},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.44234713912010193},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4100203812122345},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.18203285336494446},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.14072000980377197},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10155653953552246}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7301954030990601},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6999853253364563},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6793112754821777},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.649238109588623},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.6129288673400879},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.5241742134094238},{"id":"https://openalex.org/C2778403875","wikidata":"https://www.wikidata.org/wiki/Q20312394","display_name":"Adversarial machine learning","level":3,"score":0.5044182538986206},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.48500868678092957},{"id":"https://openalex.org/C55439883","wikidata":"https://www.wikidata.org/wiki/Q360812","display_name":"Correctness","level":2,"score":0.47336336970329285},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.45748773217201233},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.44234713912010193},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4100203812122345},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.18203285336494446},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.14072000980377197},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10155653953552246},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3580305.3599297","is_oa":true,"landing_page_url":"http://dx.doi.org/10.1145/3580305.3599297","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3580305.3599297","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 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3580305.3599297","is_oa":true,"landing_page_url":"http://dx.doi.org/10.1145/3580305.3599297","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3580305.3599297","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 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.5400000214576721,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4385568142.pdf","grobid_xml":"https://content.openalex.org/works/W4385568142.grobid-xml"},"referenced_works_count":18,"referenced_works":["https://openalex.org/W1785882633","https://openalex.org/W2073307618","https://openalex.org/W2077870633","https://openalex.org/W2115802951","https://openalex.org/W2132555912","https://openalex.org/W2134701672","https://openalex.org/W2807363941","https://openalex.org/W2952437217","https://openalex.org/W2952573398","https://openalex.org/W2973217491","https://openalex.org/W2997591727","https://openalex.org/W3012794253","https://openalex.org/W4212774754","https://openalex.org/W4248991387","https://openalex.org/W4285070145","https://openalex.org/W4320858528","https://openalex.org/W4379089711","https://openalex.org/W4385568142"],"related_works":["https://openalex.org/W3048732067","https://openalex.org/W4383468834","https://openalex.org/W2900159906","https://openalex.org/W4384648009","https://openalex.org/W4283221438","https://openalex.org/W4287828318","https://openalex.org/W2406556600","https://openalex.org/W4380352238","https://openalex.org/W3126470649","https://openalex.org/W2930249865"],"abstract_inverted_index":{"Causality-informed":[0],"machine":[1,17,36],"learning":[2,37,73,194],"has":[3,57],"been":[4],"proposed":[5],"as":[6],"an":[7],"avenue":[8],"for":[9,87,158],"achieving":[10],"many":[11],"of":[12,15,34,41,62,74,116,161,173,185],"the":[13,39,72,82,110,117,123,142,147,159,183],"goals":[14],"modern":[16],"learning,":[18],"from":[19,44],"ensuring":[20],"generalization":[21],"under":[22],"domain":[23],"shifts":[24],"to":[25,70,121,140,146],"attaining":[26],"fairness,":[27],"robustness,":[28],"and":[29,164,178],"interpretability.":[30],"A":[31],"key":[32],"component":[33],"causal":[35,42,76,125,192],"is":[38,91,100,169],"inference":[40],"structures":[43,126],"observational":[45],"data;":[46],"in":[47,127],"practice,":[48],"this":[49,97,137,150],"data":[50,66,83,120,180],"may":[51,67],"be":[52,68,85,141],"incompletely":[53],"observed.":[54],"Prior":[55],"work":[56,103],"demonstrated":[58],"that":[59],"adversarial":[60,98,186],"perturbations":[61],"completely":[63],"observed":[64],"training":[65,119],"used":[69],"force":[71],"inaccurate":[75],"structural":[77],"models":[78],"(SCMs).":[79],"However,":[80],"when":[81],"can":[84],"audited":[86],"correctness":[88],"(e.g.,":[89],"it":[90],"cryptographically":[92],"signed":[93,133],"by":[94],"its":[95],"source),":[96],"mechanism":[99],"invalidated.":[101],"This":[102],"introduces":[104],"a":[105,114,128,165],"novel":[106],"attack":[107,154],"methodology":[108],"wherein":[109],"adversary":[111],"deceptively":[112],"omits":[113],"portion":[115],"true":[118],"bias":[122],"learned":[124],"desired":[129],"manner":[130],"(under":[131],"strong":[132],"sample":[134],"input":[135],"validation,":[136],"behavior":[138],"seems":[139],"only":[143],"strategy":[144],"available":[145],"adversary).":[148],"Under":[149],"model,":[151],"theoretically":[152],"sound":[153],"mechanisms":[155],"are":[156],"derived":[157],"case":[160],"arbitrary":[162],"SCMs,":[163],"sample-efficient":[166],"learning-based":[167],"heuristic":[168],"given.":[170],"Experimental":[171],"validation":[172],"these":[174],"approaches":[175],"on":[176],"real":[177],"synthetic":[179],"sets":[181],"demonstrates":[182],"effectiveness":[184],"missingness":[187],"attacks":[188],"at":[189],"deceiving":[190],"popular":[191],"structure":[193],"algorithms.":[195]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2025-12-19T19:40:27.379048","created_date":"2025-10-10T00:00:00"}
