{"id":"https://openalex.org/W4401863347","doi":"https://doi.org/10.1145/3637528.3671556","title":"Metric Decomposition in A/B Tests","display_name":"Metric Decomposition in A/B Tests","publication_year":2024,"publication_date":"2024-08-24","ids":{"openalex":"https://openalex.org/W4401863347","doi":"https://doi.org/10.1145/3637528.3671556"},"language":"en","primary_location":{"id":"doi:10.1145/3637528.3671556","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3637528.3671556","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","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/A5075519075","display_name":"Alex Deng","orcid":"https://orcid.org/0000-0002-8116-5602"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alex Deng","raw_affiliation_strings":["Airbnb, Seattle, WA, USA"],"raw_orcid":"https://orcid.org/0000-0002-8116-5602","affiliations":[{"raw_affiliation_string":"Airbnb, Seattle, WA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081609467","display_name":"Luke Hagar","orcid":"https://orcid.org/0000-0002-1093-9463"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Luke Hagar","raw_affiliation_strings":["University of Waterloo, Waterloo, ON, Canada"],"raw_orcid":"https://orcid.org/0000-0002-1093-9463","affiliations":[{"raw_affiliation_string":"University of Waterloo, Waterloo, ON, Canada","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071761063","display_name":"Nathaniel T. Stevens","orcid":"https://orcid.org/0000-0001-6149-5797"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Nathaniel T. Stevens","raw_affiliation_strings":["University of Waterloo, Waterloo, ON, Canada"],"raw_orcid":"https://orcid.org/0000-0001-6149-5797","affiliations":[{"raw_affiliation_string":"University of Waterloo, Waterloo, ON, Canada","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056889195","display_name":"Tatiana Xifara","orcid":"https://orcid.org/0000-0002-9494-3469"},"institutions":[{"id":"https://openalex.org/I106110158","display_name":"Bay Area Air Quality Management District","ror":"https://ror.org/04431t173","country_code":"US","type":"government","lineage":["https://openalex.org/I106110158"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tatiana Xifara","raw_affiliation_strings":["Airbnb, San Francisco, CA, USA"],"raw_orcid":"https://orcid.org/0000-0002-9494-3469","affiliations":[{"raw_affiliation_string":"Airbnb, San Francisco, CA, USA","institution_ids":["https://openalex.org/I106110158"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109264461","display_name":"Amit Gandhi","orcid":null},"institutions":[{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Amit Gandhi","raw_affiliation_strings":["University of Pennsylvania, Philadelphia, PA, USA"],"raw_orcid":"https://orcid.org/0009-0000-1179-7316","affiliations":[{"raw_affiliation_string":"University of Pennsylvania, Philadelphia, PA, USA","institution_ids":["https://openalex.org/I79576946"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.813,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.85861764,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"4885","last_page":"4895"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9950000047683716,"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/T10136","display_name":"Statistical Methods and Inference","score":0.9950000047683716,"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/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.9923999905586243,"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":0.9876000285148621,"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/decomposition","display_name":"Decomposition","score":0.718457818031311},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.6047504544258118},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5214797258377075},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.33217522501945496},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09626036882400513},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.09095904231071472}],"concepts":[{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.718457818031311},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6047504544258118},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5214797258377075},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.33217522501945496},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09626036882400513},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.09095904231071472},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3637528.3671556","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3637528.3671556","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1510659740","https://openalex.org/W1896407793","https://openalex.org/W1971189973","https://openalex.org/W1981457167","https://openalex.org/W2118267850","https://openalex.org/W2119303750","https://openalex.org/W2126002144","https://openalex.org/W2509860763","https://openalex.org/W2575883117","https://openalex.org/W2943010219","https://openalex.org/W2946387282","https://openalex.org/W2964213576","https://openalex.org/W2979658147","https://openalex.org/W3101586444","https://openalex.org/W3133591098","https://openalex.org/W3211899127","https://openalex.org/W4244038801","https://openalex.org/W4288429814","https://openalex.org/W4290927948","https://openalex.org/W4321485432","https://openalex.org/W4385562609"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W4391375266","https://openalex.org/W1979597421","https://openalex.org/W2007980826","https://openalex.org/W2061531152","https://openalex.org/W3002753104","https://openalex.org/W2077600819","https://openalex.org/W2142036596","https://openalex.org/W2072657027","https://openalex.org/W2600246793"],"abstract_inverted_index":{"More":[0],"than":[1],"a":[2,60,71,99,110],"decade":[3],"ago,":[4],"CUPED":[5,50,65],"(Controlled":[6],"Experiments":[7],"Utilizing":[8],"Pre-Experiment":[9],"Data)":[10],"mainstreamed":[11],"the":[12,36,45,125,153],"idea":[13],"of":[14,113,127,147],"variance":[15,46],"reduction":[16,47],"leveraging":[17],"pre-experiment":[18],"covariates.":[19],"Since":[20],"its":[21],"introduction,":[22],"it":[23,39],"has":[24,59],"been":[25],"implemented,":[26],"extended,":[27],"and":[28,58,135],"modernized":[29],"by":[30,42,56,97],"major":[31],"online":[32],"experimentation":[33],"platforms.":[34],"Despite":[35],"wide":[37],"adoption,":[38],"is":[40,88,115,130],"known":[41],"practitioners":[43],"that":[44],"rate":[48],"from":[49],"utilizing":[51,75],"pre-experimental":[52],"data":[53],"varies":[54],"case":[55,57],"theoretical":[61],"limit.":[62],"In":[63,90],"theory,":[64],"can":[66],"be":[67],"extended":[68],"to":[69,83,161],"augment":[70],"treatment":[72,106],"effect":[73,107],"estimator":[74],"in-experiment":[76],"data,":[77],"but":[78],"practical":[79],"guidance":[80],"on":[81],"how":[82],"construct":[84],"such":[85],"an":[86,162],"augmentation":[87,108],"lacking.":[89],"this":[91,95,128],"article,":[92],"we":[93],"fill":[94],"gap":[96],"proposing":[98],"new":[100],"direction":[101],"for":[102],"sensitivity":[103],"improvement":[104],"via":[105],"whereby":[109],"target":[111],"metric":[112,148,157],"interest":[114],"decomposed":[116],"into":[117],"components":[118],"with":[119],"high":[120],"signal-to-noise":[121],"disparity.":[122],"Inference":[123],"in":[124,155],"context":[126],"decomposition":[129,158],"developed":[131],"using":[132],"both":[133],"frequentist":[134],"Bayesian":[136],"theory.":[137],"We":[138],"provide":[139],"three":[140],"real":[141],"world":[142],"applications":[143,151],"demonstrating":[144],"different":[145],"flavors":[146],"decomposition;":[149],"these":[150],"illustrate":[152],"gain":[154],"agility":[156],"yields":[159],"relative":[160],"un-decomposed":[163],"analysis.":[164]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
