{"id":"https://openalex.org/W7133228016","doi":"https://doi.org/10.52202/075280-0842","title":"Synthetic Combinations: A Causal Inference Framework for Combinatorial Interventions","display_name":"Synthetic Combinations: A Causal Inference Framework for Combinatorial Interventions","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W7133228016","doi":"https://doi.org/10.52202/075280-0842"},"language":"en","primary_location":{"id":"doi:10.52202/075280-0842","doi":"https://doi.org/10.52202/075280-0842","is_oa":false,"landing_page_url":"https://doi.org/10.52202/075280-0842","pdf_url":null,"source":{"id":"https://openalex.org/S4363606243","display_name":"neural information processing systems","issn_l":"1049-5258","issn":["1049-5258"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-2"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Advances in Neural Information Processing Systems 36","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2303.14226","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Abhineet Agarwal","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Abhineet Agarwal","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038657171","display_name":"Anish Agarwal","orcid":"https://orcid.org/0000-0003-3919-479X","observed_orcids":["https://orcid.org/0000-0003-3919-479X"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Anish Agarwal","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Suhas Vijaykumar","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Suhas Vijaykumar","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.60599684,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"19195","last_page":"19216"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10845","display_name":"Advanced Causal Inference Techniques","score":0.9951000213623047,"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":0.9951000213623047,"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":0.0006000000284984708,"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/T11235","display_name":"Statistical Methods in Clinical Trials","score":0.00039999998989515007,"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/causal-inference","display_name":"causal inference","score":0.9760000109672546},{"id":"https://openalex.org/keywords/causal-discovery","display_name":"causal discovery","score":0.41499999165534973}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5663999915122986},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4916999936103821},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.4284000098705292},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3822000026702881},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.37720000743865967},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.37689998745918274},{"id":"https://openalex.org/C64357122","wikidata":"https://www.wikidata.org/wiki/Q1149766","display_name":"Causality (physics)","level":2,"score":0.29739999771118164},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.28929999470710754},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2797999978065491},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2766999900341034},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.27070000767707825},{"id":"https://openalex.org/C527412718","wikidata":"https://www.wikidata.org/wiki/Q855395","display_name":"Interpretation (philosophy)","level":2,"score":0.26489999890327454},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.26269999146461487},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.2619999945163727}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.52202/075280-0842","doi":"https://doi.org/10.52202/075280-0842","is_oa":false,"landing_page_url":"https://doi.org/10.52202/075280-0842","pdf_url":null,"source":{"id":"https://openalex.org/S4363606243","display_name":"neural information processing systems","issn_l":"1049-5258","issn":["1049-5258"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-2"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Advances in Neural Information Processing Systems 36","raw_type":"proceedings-article"},{"id":"pmh:oai:RePEc:arx:papers:2303.14226","doi":null,"is_oa":false,"landing_page_url":"http://arxiv.org/pdf/2303.14226","pdf_url":"http://arxiv.org/pdf/2303.14226","source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"preprint"},{"id":"pmh:oai:arXiv.org:2303.14226","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2303.14226","pdf_url":"https://arxiv.org/pdf/2303.14226","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2303.14226","doi":"https://doi.org/10.48550/arxiv.2303.14226","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2303.14226","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2303.14226","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2303.14226","pdf_url":"https://arxiv.org/pdf/2303.14226","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7133228016.pdf","grobid_xml":"https://content.openalex.org/works/W7133228016.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4404314251","https://openalex.org/W7133185587","https://openalex.org/W3101459668","https://openalex.org/W4307079009","https://openalex.org/W3213422393","https://openalex.org/W4399198646","https://openalex.org/W3167183573","https://openalex.org/W147740693","https://openalex.org/W4297688659","https://openalex.org/W30907012"],"abstract_inverted_index":{"Consider":[0],"a":[1,34,39,45,95,100,117,228,239,258],"setting":[2],"where":[3,254],"there":[4,87],"are":[5],"$N$":[6,79],"heterogeneous":[7],"units":[8,126],"and":[9,80,137,175,181],"$p$":[10,25,81,261],"interventions.":[11],"Our":[12,191],"goal":[13],"is":[14,38,73,88,97,102,133,152,178,257],"to":[15,68,226,250],"learn":[16],"unit-specific":[17],"potential":[18,106,131,150],"outcomes":[19,132,151],"for":[20,159],"any":[21],"combination":[22,35,56,101],"of":[23,36,47,130,139,148],"these":[24,113],"interventions,":[26],"i.e.,":[27,91],"$N":[28,64,161],"\\times":[29,65,162,198,213,218],"2^p$":[30,66,163],"causal":[31,252],"parameters.":[32],"Choosing":[33],"interventions":[37,140],"problem":[40],"that":[41,109,122],"naturally":[42],"arises":[43],"in":[44,58,144],"variety":[46],"applications":[48],"such":[49],"as":[50,78,211],"factorial":[51],"design":[52],"experiments,":[53],"recommendation":[54],"engines,":[55],"therapies":[57],"medicine,":[59],"conjoint":[60],"analysis,":[61],"etc.":[62],"Running":[63],"experiments":[67],"estimate":[69],"the":[70,128,142,145,149,188,255],"various":[71],"parameters":[72,164],"likely":[74,89],"expensive":[75],"and/or":[76],"infeasible":[77],"grow.":[82],"Further,":[83],"with":[84,104],"observational":[85],"data":[86],"confounding,":[90],"whether":[92],"or":[93],"not":[94],"unit":[96],"seen":[98],"under":[99,108,184],"correlated":[103],"its":[105],"outcome":[107],"combination.":[110],"To":[111],"address":[112],"challenges,":[114],"we":[115,246],"propose":[116,169,227],"novel":[118],"latent":[119],"factor":[120],"model":[121],"imposes":[123],"structure":[124],"across":[125],"(i.e.,":[127,141],"matrix":[129],"approximately":[134,153],"rank":[135],"$r$),":[136],"combinations":[138],"coefficients":[143],"Fourier":[146],"expansion":[147],"$s$":[154],"sparse).":[155],"We":[156,168,222],"establish":[157,176],"identification":[158],"all":[160],"despite":[165],"unobserved":[166],"confounding.":[167],"an":[170],"estimation":[171,195],"procedure,":[172],"Synthetic":[173,224,233],"Combinations,":[174],"it":[177],"finite-sample":[179],"consistent":[180,194],"asymptotically":[182],"normal":[183],"precise":[185],"conditions":[186],"on":[187,238,242],"observation":[189],"pattern.":[190],"results":[192],"imply":[193],"given":[196],"$\\text{poly}(r)":[197],"\\left(":[199],"N":[200],"+":[201,220],"s^2p\\right)$":[202],"observations,":[203],"while":[204],"previous":[205],"methods":[206],"have":[207],"sample":[208],"complexity":[209],"scaling":[210],"$\\min(N":[212],"s^2p,":[214],"\\":[215,216],"\\text{poly(r)}":[217],"(N":[219],"2^p))$.":[221],"use":[223],"Combinations":[225,234],"data-efficient":[229],"experimental":[230],"design.":[231],"Empirically,":[232],"outperforms":[235],"competing":[236],"approaches":[237],"real-world":[240],"dataset":[241],"movie":[243],"recommendations.":[244],"Lastly,":[245],"extend":[247],"our":[248],"analysis":[249],"do":[251],"inference":[253],"intervention":[256],"permutation":[259],"over":[260],"items":[262],"(e.g.,":[263],"rankings).":[264]},"counts_by_year":[],"updated_date":"2026-10-14T16:39:01.820549","created_date":"2025-12-08T00:00:00"}