{"id":"https://openalex.org/W4385562609","doi":"https://doi.org/10.1145/3580305.3599928","title":"Variance Reduction Using In-Experiment Data: Efficient and Targeted Online Measurement for Sparse and Delayed Outcomes","display_name":"Variance Reduction Using In-Experiment Data: Efficient and Targeted Online Measurement for Sparse and Delayed Outcomes","publication_year":2023,"publication_date":"2023-08-04","ids":{"openalex":"https://openalex.org/W4385562609","doi":"https://doi.org/10.1145/3580305.3599928"},"language":"en","primary_location":{"id":"doi:10.1145/3580305.3599928","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3580305.3599928","pdf_url":null,"source":null,"license":null,"license_id":null,"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":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":true,"raw_author_name":"Alex Deng","raw_affiliation_strings":["Airbnb, Seattle, WA, USA"],"affiliations":[{"raw_affiliation_string":"Airbnb, Seattle, WA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048623684","display_name":"Michelle Du","orcid":"https://orcid.org/0009-0006-0142-6718"},"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":"Michelle Du","raw_affiliation_strings":["Airbnb, San Francisco, CA, USA"],"affiliations":[{"raw_affiliation_string":"Airbnb, San Francisco, CA, USA","institution_ids":["https://openalex.org/I106110158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019694176","display_name":"Anna Matlin","orcid":"https://orcid.org/0009-0002-3381-9672"},"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":"Anna Matlin","raw_affiliation_strings":["Airbnb, San Francisco, CA, USA"],"affiliations":[{"raw_affiliation_string":"Airbnb, San Francisco, CA, USA","institution_ids":["https://openalex.org/I106110158"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035621099","display_name":"Qing Zhang","orcid":"https://orcid.org/0009-0005-3572-2606"},"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":"Qing Zhang","raw_affiliation_strings":["Airbnb, San Francisco, CA, USA"],"affiliations":[{"raw_affiliation_string":"Airbnb, San Francisco, CA, USA","institution_ids":["https://openalex.org/I106110158"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5075519075"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.4339,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.9006784,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"3937","last_page":"3946"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10845","display_name":"Advanced Causal Inference Techniques","score":0.9955000281333923,"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":0.9955000281333923,"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":0.989799976348877,"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/T11542","display_name":"Behavioral Health and Interventions","score":0.941100001335144,"subfield":{"id":"https://openalex.org/subfields/3202","display_name":"Applied Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/covariate","display_name":"Covariate","score":0.763446569442749},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.7186333537101746},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.6759803891181946},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6372829675674438},{"id":"https://openalex.org/keywords/variance-reduction","display_name":"Variance reduction","score":0.6278201937675476},{"id":"https://openalex.org/keywords/sample-variance","display_name":"Sample variance","score":0.6045368313789368},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.5768271684646606},{"id":"https://openalex.org/keywords/statistical-power","display_name":"Statistical power","score":0.5759979486465454},{"id":"https://openalex.org/keywords/outcome","display_name":"Outcome (game theory)","score":0.5001280307769775},{"id":"https://openalex.org/keywords/counterfactual-thinking","display_name":"Counterfactual thinking","score":0.48047250509262085},{"id":"https://openalex.org/keywords/sample-size-determination","display_name":"Sample size determination","score":0.45816829800605774},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.45758309960365295},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.4546227753162384},{"id":"https://openalex.org/keywords/product","display_name":"Product (mathematics)","score":0.42355138063430786},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.36975276470184326},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.22293514013290405},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.22098517417907715},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.11774665117263794},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11366179585456848}],"concepts":[{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.763446569442749},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.7186333537101746},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6759803891181946},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6372829675674438},{"id":"https://openalex.org/C62644790","wikidata":"https://www.wikidata.org/wiki/Q3454689","display_name":"Variance reduction","level":3,"score":0.6278201937675476},{"id":"https://openalex.org/C2993021520","wikidata":"https://www.wikidata.org/wiki/Q175199","display_name":"Sample variance","level":3,"score":0.6045368313789368},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.5768271684646606},{"id":"https://openalex.org/C96608239","wikidata":"https://www.wikidata.org/wiki/Q1199823","display_name":"Statistical power","level":2,"score":0.5759979486465454},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.5001280307769775},{"id":"https://openalex.org/C108650721","wikidata":"https://www.wikidata.org/wiki/Q1783253","display_name":"Counterfactual thinking","level":2,"score":0.48047250509262085},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.45816829800605774},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.45758309960365295},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.4546227753162384},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.42355138063430786},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36975276470184326},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.22293514013290405},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22098517417907715},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.11774665117263794},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11366179585456848},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"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/C144237770","wikidata":"https://www.wikidata.org/wiki/Q747534","display_name":"Mathematical economics","level":1,"score":0.0},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","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/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3580305.3599928","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3580305.3599928","pdf_url":null,"source":null,"license":null,"license_id":null,"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":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W1841840321","https://openalex.org/W1975566260","https://openalex.org/W2015374677","https://openalex.org/W2021866613","https://openalex.org/W2040367556","https://openalex.org/W2063910719","https://openalex.org/W2070815407","https://openalex.org/W2080770071","https://openalex.org/W2110228583","https://openalex.org/W2118267850","https://openalex.org/W2119303750","https://openalex.org/W2126002144","https://openalex.org/W2157641118","https://openalex.org/W2163996478","https://openalex.org/W2218578097","https://openalex.org/W2509295096","https://openalex.org/W2509860763","https://openalex.org/W2517816274","https://openalex.org/W2532610538","https://openalex.org/W2792341222","https://openalex.org/W2799155063","https://openalex.org/W2809468631","https://openalex.org/W2914779069","https://openalex.org/W2943010219","https://openalex.org/W2946387282","https://openalex.org/W2949574684","https://openalex.org/W2952127798","https://openalex.org/W2963256194","https://openalex.org/W2963805801","https://openalex.org/W3099125911","https://openalex.org/W3101586444","https://openalex.org/W3104023681","https://openalex.org/W4214717370","https://openalex.org/W4220767012","https://openalex.org/W4248536918","https://openalex.org/W4290927948","https://openalex.org/W4310299640"],"related_works":["https://openalex.org/W3201448254","https://openalex.org/W4286970243","https://openalex.org/W2066431708","https://openalex.org/W3025615835","https://openalex.org/W4384133558","https://openalex.org/W173210993","https://openalex.org/W2390660599","https://openalex.org/W3003410553","https://openalex.org/W4372272639","https://openalex.org/W2321238704"],"abstract_inverted_index":{"Improving":[0],"statistical":[1],"power":[2,28],"is":[3,35,128,149,162],"a":[4,129,132,142,150,160],"common":[5],"challenge":[6,100],"for":[7,64,90,116,131],"online":[8,51],"experimentation":[9,52,209],"platforms":[10],"so":[11],"that":[12,119,155,159,176],"more":[13,185,213],"hypotheses":[14],"can":[15,22,84,178],"be":[16,23,85],"tested":[17],"and":[18,79,174,215],"lower":[19],"effect":[20],"sizes":[21],"detected.":[24],"To":[25],"increase":[26],"the":[27,31,39,55,67,99,157,165,188,199],"without":[29],"increasing":[30],"sample":[32],"size,":[33],"it":[34],"necessary":[36],"to":[37,50,62,107,171,187,211],"consider":[38],"variance":[40,117,180,196],"of":[41,57,101,182],"experimental":[42],"outcome":[43,190],"metrics.":[44,69],"Variance":[45],"reduction":[46,118,181,194],"was":[47],"previously":[48],"applied":[49,168],"based":[53],"on":[54,74,122],"idea":[56],"using":[58],"pre-experiment":[59,77],"covariate":[60],"data":[61],"account":[63],"noise":[65],"in":[66,195,203],"final":[68],"Since":[70],"this":[71,204],"method":[72,127,148],"relies":[73],"correlations":[75],"between":[76],"covariates":[78],"experiment":[80],"outcomes,":[81],"its":[82],"effectiveness":[83],"limited":[86],"when":[87],"testing":[88],"features":[89],"specific":[91],"product":[92],"surfaces.":[93],"We":[94,111,167],"were":[95],"also":[96],"motivated":[97],"by":[98,164,198],"attributing":[102],"sparse,":[103],"delayed":[104,143,189],"binary":[105,144],"outcomes":[106],"individual":[108],"user-product":[109],"interactions.":[110],"present":[112],"two":[113,200],"novel":[114],"methods":[115,170,201],"rely":[120],"exclusively":[121],"in-experiment":[123],"data.":[124],"The":[125,146,192],"first":[126],"framework":[130],"model-based":[133],"leading":[134],"indicator":[135],"metric":[136],"which":[137],"continually":[138],"estimates":[139],"progress":[140],"toward":[141],"outcome.":[145],"second":[147],"counterfactual":[151],"treatment":[152],"exposure":[153],"index":[154],"quantifies":[156],"amount":[158],"user":[161],"impacted":[163],"treatment.":[166],"these":[169],"past":[172],"experiments":[173],"found":[175],"both":[177],"achieve":[179],"50%":[183],"or":[184],"compared":[186],"metric.":[191],"substantial":[193],"afforded":[197],"presented":[202],"paper":[205],"has":[206],"enabled":[207],"Airbnb's":[208],"platform":[210],"become":[212],"agile":[214],"innovative.":[216]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
