{"id":"https://openalex.org/W4386730116","doi":"https://doi.org/10.1145/3604915.3608808","title":"Correcting for Interference in Experiments: A Case Study at Douyin","display_name":"Correcting for Interference in Experiments: A Case Study at Douyin","publication_year":2023,"publication_date":"2023-09-14","ids":{"openalex":"https://openalex.org/W4386730116","doi":"https://doi.org/10.1145/3604915.3608808"},"language":"en","primary_location":{"id":"doi:10.1145/3604915.3608808","doi":"https://doi.org/10.1145/3604915.3608808","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3604915.3608808","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3604915.3608808","source":{"id":"https://openalex.org/S7407086088","display_name":"ACM Conference on Recommender Systems (RecSys)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"Proceedings of the 17th ACM Conference on Recommender Systems","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3604915.3608808","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5037762932","display_name":"Vivek F. Farias","orcid":"https://orcid.org/0000-0002-5856-9246","observed_orcids":["https://orcid.org/0000-0002-5856-9246"]},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vivek Farias","raw_affiliation_strings":["MIT, USA"],"raw_orcid":"https://orcid.org/0000-0002-5856-9246","affiliations":[{"raw_affiliation_string":"MIT, USA","institution_ids":["https://openalex.org/I63966007"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090285131","display_name":"Hao Li","orcid":"https://orcid.org/0009-0002-7566-6811","observed_orcids":["https://orcid.org/0009-0002-7566-6811"]},"institutions":[{"id":"https://openalex.org/I4405256866","display_name":"ByteDance (China)","ror":"https://ror.org/04m8av638","country_code":"CN","type":"company","lineage":["https://openalex.org/I4405256866"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hao Li","raw_affiliation_strings":["Bytedance, China"],"raw_orcid":"https://orcid.org/0009-0002-7566-6811","affiliations":[{"raw_affiliation_string":"Bytedance, China","institution_ids":["https://openalex.org/I4405256866"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002767768","display_name":"Tianyi Peng","orcid":"https://orcid.org/0000-0002-9046-3206","observed_orcids":["https://orcid.org/0000-0002-9046-3206"]},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tianyi Peng","raw_affiliation_strings":["MIT, USA"],"raw_orcid":"https://orcid.org/0000-0002-9046-3206","affiliations":[{"raw_affiliation_string":"MIT, USA","institution_ids":["https://openalex.org/I63966007"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025604819","display_name":"Xinyuyang Ren","orcid":"https://orcid.org/0009-0008-2887-3125","observed_orcids":["https://orcid.org/0009-0008-2887-3125"]},"institutions":[{"id":"https://openalex.org/I4405256866","display_name":"ByteDance (China)","ror":"https://ror.org/04m8av638","country_code":"CN","type":"company","lineage":["https://openalex.org/I4405256866"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinyuyang Ren","raw_affiliation_strings":["Bytedance, China"],"raw_orcid":"https://orcid.org/0009-0008-2887-3125","affiliations":[{"raw_affiliation_string":"Bytedance, China","institution_ids":["https://openalex.org/I4405256866"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102899067","display_name":"Huawei Zhang","orcid":"https://orcid.org/0009-0008-6537-7171","observed_orcids":["https://orcid.org/0009-0008-6537-7171"]},"institutions":[{"id":"https://openalex.org/I4405256866","display_name":"ByteDance (China)","ror":"https://ror.org/04m8av638","country_code":"CN","type":"company","lineage":["https://openalex.org/I4405256866"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huawei Zhang","raw_affiliation_strings":["Bytedance, China"],"raw_orcid":"https://orcid.org/0009-0008-6537-7171","affiliations":[{"raw_affiliation_string":"Bytedance, China","institution_ids":["https://openalex.org/I4405256866"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038710330","display_name":"Andrew Zheng","orcid":"https://orcid.org/0000-0001-6711-553X","observed_orcids":["https://orcid.org/0000-0001-6711-553X"]},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Andrew Zheng","raw_affiliation_strings":["MIT, USA"],"raw_orcid":"https://orcid.org/0000-0001-6711-553X","affiliations":[{"raw_affiliation_string":"MIT, USA","institution_ids":["https://openalex.org/I63966007"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":5.9811,"has_fulltext":true,"cited_by_count":7,"citation_normalized_percentile":{"value":0.96475539,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"455","last_page":"466"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10845","display_name":"Advanced Causal Inference Techniques","score":0.9452999830245972,"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":0.9452999830245972,"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.022700000554323196,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11195","display_name":"Simulation Techniques and Applications","score":0.0027000000700354576,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/two-sided-markets","display_name":"two-sided markets","score":0.7799999713897705},{"id":"https://openalex.org/keywords/douyin","display_name":"Douyin","score":0.7440000176429749},{"id":"https://openalex.org/keywords/treatment-effect-estimation","display_name":"treatment effect estimation","score":0.6779999732971191},{"id":"https://openalex.org/keywords/policy-evaluation","display_name":"policy evaluation","score":0.7689999938011169},{"id":"https://openalex.org/keywords/monte-carlo-estimation","display_name":"Monte Carlo estimation","score":0.578000009059906},{"id":"https://openalex.org/keywords/taylor-expansion","display_name":"Taylor series expansion","score":0.5699999928474426},{"id":"https://openalex.org/keywords/markov-decision-process","display_name":"Markov decision process","score":0.9629999995231628},{"id":"https://openalex.org/keywords/uncertainty-quantification","display_name":"uncertainty quantification","score":0.7429999709129333}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.88479083776474},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.7215853333473206},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.625625729560852},{"id":"https://openalex.org/C32022120","wikidata":"https://www.wikidata.org/wiki/Q797225","display_name":"Interference (communication)","level":3,"score":0.6249560713768005},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5380124449729919},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.5345282554626465},{"id":"https://openalex.org/C165646398","wikidata":"https://www.wikidata.org/wiki/Q3755281","display_name":"Minimum-variance unbiased estimator","level":3,"score":0.49520888924598694},{"id":"https://openalex.org/C91306197","wikidata":"https://www.wikidata.org/wiki/Q45767","display_name":"Competition (biology)","level":2,"score":0.4405975937843323},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.439611554145813},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4340800940990448},{"id":"https://openalex.org/C971699","wikidata":"https://www.wikidata.org/wiki/Q1132714","display_name":"Delta method","level":3,"score":0.4310969114303589},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.42140763998031616},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3408549129962921},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26186424493789673},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2056761085987091},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.19927573204040527},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.10906007885932922},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.08633500337600708},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1145/3604915.3608808","doi":"https://doi.org/10.1145/3604915.3608808","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3604915.3608808","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3604915.3608808","source":{"id":"https://openalex.org/S7407086088","display_name":"ACM Conference on Recommender Systems (RecSys)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"Proceedings of the 17th ACM Conference on Recommender Systems","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2305.02542","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2305.02542","pdf_url":"https://arxiv.org/pdf/2305.02542","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:dspace.mit.edu:1721.1/152333","doi":null,"is_oa":true,"landing_page_url":"https://hdl.handle.net/1721.1/152333","pdf_url":"https://dspace.mit.edu/bitstream/1721.1/152333/1/3604915.3608808.pdf","source":{"id":"https://openalex.org/S4306400425","display_name":"DSpace@MIT (Massachusetts Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I63966007","host_organization_name":"Massachusetts Institute of Technology","host_organization_lineage":["https://openalex.org/I63966007"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Association for Computing Machinery","raw_type":"http://purl.org/eprint/type/ConferencePaper"},{"id":"doi:10.48550/arxiv.2305.02542","doi":"https://doi.org/10.48550/arxiv.2305.02542","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2305.02542","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.1145/3604915.3608808","doi":"https://doi.org/10.1145/3604915.3608808","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3604915.3608808","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3604915.3608808","source":{"id":"https://openalex.org/S7407086088","display_name":"ACM Conference on Recommender Systems (RecSys)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"Proceedings of the 17th ACM Conference on Recommender Systems","raw_type":"proceedings-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4386730116.pdf","grobid_xml":"https://content.openalex.org/works/W4386730116.grobid-xml"},"referenced_works_count":16,"referenced_works":["https://openalex.org/W146900863","https://openalex.org/W1988168828","https://openalex.org/W1990054777","https://openalex.org/W2074380434","https://openalex.org/W2107602010","https://openalex.org/W2121357720","https://openalex.org/W2125105347","https://openalex.org/W2273088453","https://openalex.org/W2744538883","https://openalex.org/W2784068709","https://openalex.org/W2896239072","https://openalex.org/W2964019528","https://openalex.org/W3034693812","https://openalex.org/W3123750779","https://openalex.org/W3157287137","https://openalex.org/W7133228510"],"related_works":["https://openalex.org/W7047039209","https://openalex.org/W7133228510","https://openalex.org/W4405464793","https://openalex.org/W4399911789","https://openalex.org/W2896239072","https://openalex.org/W4383558036","https://openalex.org/W3157287137","https://openalex.org/W3020710313","https://openalex.org/W4403966822","https://openalex.org/W4403363377"],"abstract_inverted_index":{"Interference":[0],"is":[1,103,120],"a":[2,90,123,160],"ubiquitous":[3],"problem":[4,69],"in":[5,47,54,72,105,154,166,196],"experiments":[6,74],"conducted":[7],"on":[8,59,95,149],"two-sided":[9],"content":[10],"marketplaces,":[11],"such":[12,73],"as":[13,75],"Douyin":[14],"(China\u2019s":[15],"analog":[16],"of":[17,27,62,70,77,126],"TikTok).":[18],"In":[19],"many":[20],"cases,":[21],"creators":[22,30],"are":[23,84],"the":[24,51,60,63,68,106,115,142,155,192],"natural":[25],"unit":[26],"experimentation,":[28],"but":[29,53],"interfere":[31],"with":[32],"each":[33],"other":[34],"through":[35],"competition":[36],"for":[37,129,164],"viewers\u2019":[38],"limited":[39],"time":[40],"and":[41,153,185],"attention.":[42],"\u201cNaive\u201d":[43],"estimators":[44],"currently":[45],"used":[46],"practice":[48],"simply":[49],"ignore":[50],"interference,":[52],"doing":[55],"so":[56],"incur":[57],"bias":[58,101],"order":[61],"treatment":[64,107,175],"effect.":[65],"We":[66,88],"formalize":[67],"inference":[71],"one":[76,169],"policy":[78,130],"evaluation.":[79],"Off-policy":[80],"estimators,":[81],"while":[82,109],"unbiased,":[83],"impractically":[85],"high":[86],"variance.":[87],"introduce":[89],"novel":[91],"Monte-Carlo":[92],"estimator,":[93],"based":[94],"\u201cDifferences-in-Qs\u201d":[96],"(DQ)":[97],"techniques,":[98],"which":[99,132,170],"achieves":[100],"that":[102],"second-order":[104],"effect,":[108],"remaining":[110],"sample-efficient":[111],"to":[112,121,136,191],"estimate.":[113],"On":[114,141],"theoretical":[116],"side,":[117,144],"our":[118,147,197],"contribution":[119],"develop":[122,157],"generalized":[124],"theory":[125,135],"Taylor":[127],"expansions":[128],"evaluation,":[131],"extends":[133],"DQ":[134,158],"all":[137],"major":[138],"MDP":[139],"formulations.":[140],"practical":[143],"we":[145],"implement":[146],"estimator":[148,163],"Douyin\u2019s":[150],"experimentation":[151],"platform,":[152],"process":[156],"into":[159],"truly":[161],"\u201cplug-and-play\u201d":[162],"interference":[165],"real-world":[167],"settings:":[168],"provides":[171],"robust,":[172],"low-bias,":[173],"low-variance":[174],"effect":[176],"estimates;":[177],"admits":[178],"computationally":[179],"cheap,":[180],"asymptotically":[181],"exact":[182],"uncertainty":[183],"quantification;":[184],"reduces":[186],"MSE":[187],"by":[188],"99%":[189],"compared":[190],"best":[193],"existing":[194],"alternatives":[195],"applications.":[198]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2023-09-15T00:00:00"}