{"id":"https://openalex.org/W4383557942","doi":"https://doi.org/10.1145/3580507.3597718","title":"Deep Learning Based Causal Inference for Large-Scale Combinatorial Experiments: Theory and Empirical Evidence","display_name":"Deep Learning Based Causal Inference for Large-Scale Combinatorial Experiments: Theory and Empirical Evidence","publication_year":2023,"publication_date":"2023-07-07","ids":{"openalex":"https://openalex.org/W4383557942","doi":"https://doi.org/10.1145/3580507.3597718"},"language":"en","primary_location":{"id":"doi:10.1145/3580507.3597718","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3580507.3597718","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 24th ACM Conference on Economics and Computation","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"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/A5072743763","display_name":"Zikun Ye","orcid":"https://orcid.org/0000-0001-9914-7966"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Zikun Ye","raw_affiliation_strings":["University of Illinois Urbana Champaign, Urbana, United States of America"],"raw_orcid":"https://orcid.org/0000-0001-9914-7966","affiliations":[{"raw_affiliation_string":"University of Illinois Urbana Champaign, Urbana, United States of America","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043209618","display_name":"Z. Zhang","orcid":"https://orcid.org/0009-0005-4566-8148"},"institutions":[{"id":"https://openalex.org/I204465549","display_name":"Washington University in St. Louis","ror":"https://ror.org/01yc7t268","country_code":"US","type":"education","lineage":["https://openalex.org/I204465549"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhiqi Zhang","raw_affiliation_strings":["Washington University in St. Louis, St. Louis, USA"],"raw_orcid":"https://orcid.org/0009-0005-4566-8148","affiliations":[{"raw_affiliation_string":"Washington University in St. Louis, St. Louis, USA","institution_ids":["https://openalex.org/I204465549"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084405003","display_name":"Dennis Zhang","orcid":"https://orcid.org/0000-0002-4544-775X"},"institutions":[{"id":"https://openalex.org/I204465549","display_name":"Washington University in St. Louis","ror":"https://ror.org/01yc7t268","country_code":"US","type":"education","lineage":["https://openalex.org/I204465549"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dennis J. Zhang","raw_affiliation_strings":["Washington University in St. Louis, St. Louis, United States of America"],"raw_orcid":"https://orcid.org/0000-0002-4544-775X","affiliations":[{"raw_affiliation_string":"Washington University in St. Louis, St. Louis, United States of America","institution_ids":["https://openalex.org/I204465549"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076606979","display_name":"Heng Zhang","orcid":"https://orcid.org/0000-0002-6105-6994"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Heng Zhang","raw_affiliation_strings":["Arizona State University, Tempe, USA"],"raw_orcid":"https://orcid.org/0000-0002-6105-6994","affiliations":[{"raw_affiliation_string":"Arizona State University, Tempe, USA","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103062025","display_name":"Renyu Zhang","orcid":"https://orcid.org/0000-0003-0284-164X"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Renyu Zhang","raw_affiliation_strings":["The Chinese University of Hong Kong, Hong Kong, China"],"raw_orcid":"https://orcid.org/0000-0003-0284-164X","affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I177725633"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5072743763"],"corresponding_institution_ids":["https://openalex.org/I157725225"],"apc_list":null,"apc_paid":null,"fwci":1.6466,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.84825509,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1160","last_page":"1160"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10845","display_name":"Advanced Causal Inference Techniques","score":0.9916999936103821,"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":0.9916999936103821,"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/T11235","display_name":"Statistical Methods in Clinical Trials","score":0.9197999835014343,"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":0.8077794909477234},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6685179471969604},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6325637102127075},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6198232769966125},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.6176004409790039},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5921408534049988},{"id":"https://openalex.org/keywords/randomized-experiment","display_name":"Randomized experiment","score":0.5857237577438354},{"id":"https://openalex.org/keywords/orthogonality","display_name":"Orthogonality","score":0.5665818452835083},{"id":"https://openalex.org/keywords/average-treatment-effect","display_name":"Average treatment effect","score":0.5292047262191772},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5025873184204102},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.4480460584163666},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4447435140609741},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.22870177030563354},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.1751352846622467},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.16408628225326538}],"concepts":[{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.8077794909477234},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6685179471969604},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6325637102127075},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6198232769966125},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6176004409790039},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5921408534049988},{"id":"https://openalex.org/C155108698","wikidata":"https://www.wikidata.org/wiki/Q1231081","display_name":"Randomized experiment","level":2,"score":0.5857237577438354},{"id":"https://openalex.org/C17137986","wikidata":"https://www.wikidata.org/wiki/Q215067","display_name":"Orthogonality","level":2,"score":0.5665818452835083},{"id":"https://openalex.org/C89337504","wikidata":"https://www.wikidata.org/wiki/Q4828276","display_name":"Average treatment effect","level":3,"score":0.5292047262191772},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5025873184204102},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.4480460584163666},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4447435140609741},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22870177030563354},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.1751352846622467},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.16408628225326538},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"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:10.1145/3580507.3597718","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3580507.3597718","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 24th ACM Conference on Economics and Computation","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4389471064","https://openalex.org/W2119346805","https://openalex.org/W3005312434","https://openalex.org/W4313559754","https://openalex.org/W2147630691","https://openalex.org/W4323042385","https://openalex.org/W1730782591","https://openalex.org/W4388998033","https://openalex.org/W2510789027","https://openalex.org/W3160537436"],"abstract_inverted_index":{"Large-scale":[0],"online":[1],"platforms":[2],"launch":[3],"hundreds":[4],"of":[5,23,36,46,56,83,99,178,187,207],"randomized":[6],"experiments":[7,171],"(a.k.a.":[8],"A/B":[9],"tests)":[10],"every":[11],"day":[12],"to":[13,51,78,197,212],"iterate":[14],"their":[15],"operations":[16],"and":[17,39,60,74,113,116,131,165,200,211],"marketing":[18],"strategies,":[19],"while":[20],"the":[21,44,53,62,80,91,142,202,214],"combinations":[22],"these":[24],"treatments":[25,174,179],"are":[26],"typically":[27],"not":[28],"exhaustively":[29],"tested.":[30,181],"It":[31],"triggers":[32],"an":[33],"important":[34],"question":[35],"both":[37],"academic":[38],"practical":[40],"interests:":[41],"Without":[42],"observing":[43,94,148,184],"outcomes":[45],"all":[47],"treatment":[48,58,64,85,100,144,188,204,209,216],"combinations,":[49,189],"how":[50],"estimate":[52,79,199],"causal":[54,81],"effect":[55,82,205],"any":[57,84,208],"combination":[59,86,145,177,210],"identify":[61,213],"optimal":[63,215],"combination?":[65],"We":[66,123],"develop":[67],"a":[68,96,149,161,185],"novel":[69],"framework":[70,104,128,168],"combining":[71],"deep":[72,107,121],"learning":[73,77],"double":[75],"machine":[76],"for":[87,140,169],"each":[88,176],"user":[89],"on":[90],"platform":[92,164],"when":[93,146],"only":[95,147,183],"small":[97],"subset":[98,186],"combinations.":[101,151],"Our":[102],"proposed":[103],"(called":[105],"debiased":[106],"learning,":[108],"DeDL)":[109],"exploits":[110],"Neyman":[111],"orthogonality":[112],"combines":[114],"interpretable":[115],"flexible":[117],"structural":[118],"layers":[119],"in":[120],"learning.":[122],"prove":[124],"theoretically":[125],"that":[126],"this":[127],"yields":[129],"consistent":[130],"asymptotically":[132],"normal":[133],"estimators":[134],"under":[135],"mild":[136],"assumptions,":[137],"thus":[138],"allowing":[139],"identifying":[141],"best":[143],"few":[150],"To":[152],"empirically":[153],"validate":[154],"our":[155,167,190],"method,":[156],"we":[157],"then":[158],"collaborate":[159],"with":[160],"large-scale":[162],"video-sharing":[163],"implement":[166],"three":[170,173],"involving":[172],"where":[175],"is":[180],"When":[182],"DeDL":[191],"approach":[192],"significantly":[193],"outperforms":[194],"other":[195],"benchmarks":[196],"accurately":[198],"infer":[201],"average":[203],"(ATE)":[206],"combination.":[217]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
