{"id":"https://openalex.org/W7134837378","doi":"https://doi.org/10.48550/arxiv.2603.07018","title":"TEA-Time: Transporting Effects Across Time","display_name":"TEA-Time: Transporting Effects Across Time","publication_year":2026,"publication_date":"2026-03-07","ids":{"openalex":"https://openalex.org/W7134837378","doi":"https://doi.org/10.48550/arxiv.2603.07018"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2603.07018","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"Article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5128662085","display_name":"Harsh Parikh","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Parikh, Harsh","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040104942","display_name":"Gabriel Levin-Konigsberg","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Levin-Konigsberg, Gabriel","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004130134","display_name":"Dominique Perrault-Joncas","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Perrault-Joncas, Dominique","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5056559023","display_name":"Alexander Volfovsky","orcid":"https://orcid.org/0000-0003-4462-1020"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Volfovsky, Alexander","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5128662085"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10845","display_name":"Advanced Causal Inference Techniques","score":0.987500011920929,"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":0.987500011920929,"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":0.0012000000569969416,"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/T10136","display_name":"Statistical Methods and Inference","score":0.0007999999797903001,"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/estimator","display_name":"Estimator","score":0.7426999807357788},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.5325999855995178},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.49790000915527344},{"id":"https://openalex.org/keywords/average-treatment-effect","display_name":"Average treatment effect","score":0.47679999470710754},{"id":"https://openalex.org/keywords/treatment-effect","display_name":"Treatment effect","score":0.44780001044273376},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.40849998593330383}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.7426999807357788},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.5849000215530396},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.5367000102996826},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.5325999855995178},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.49790000915527344},{"id":"https://openalex.org/C89337504","wikidata":"https://www.wikidata.org/wiki/Q4828276","display_name":"Average treatment effect","level":3,"score":0.47679999470710754},{"id":"https://openalex.org/C2987370644","wikidata":"https://www.wikidata.org/wiki/Q7836903","display_name":"Treatment effect","level":2,"score":0.44780001044273376},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4104999899864197},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.40849998593330383},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.39480000734329224},{"id":"https://openalex.org/C2993060064","wikidata":"https://www.wikidata.org/wiki/Q49918","display_name":"Population mean","level":3,"score":0.28459998965263367},{"id":"https://openalex.org/C168563851","wikidata":"https://www.wikidata.org/wiki/Q1436668","display_name":"Randomized controlled trial","level":2,"score":0.27160000801086426},{"id":"https://openalex.org/C155108698","wikidata":"https://www.wikidata.org/wiki/Q1231081","display_name":"Randomized experiment","level":2,"score":0.25920000672340393}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2603.07018","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"Article"},{"id":"doi:10.48550/arxiv.2603.07018","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.07018","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2603.07018","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"Article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Treatment":[0],"effects":[1,35,59],"estimated":[2],"from":[3,140],"randomized":[4],"controlled":[5],"trials":[6,83],"are":[7],"local":[8],"not":[9],"only":[10],"to":[11,17,36,137],"the":[12,18,22,46,61,85,120,141,147,154],"study":[13],"population":[14],"but":[15,161],"also":[16],"time":[19,37,98],"at":[20,88],"which":[21],"trial":[23],"was":[24,42],"conducted.":[25,43],"We":[26,44,74,133],"develop":[27,101],"a":[28,56,71,151],"framework":[29],"for":[30,107],"temporal":[31,58,72,170],"transportation:":[32],"extrapolating":[33],"treatment":[34,49,68,94],"periods":[38],"where":[39],"no":[40],"experiment":[41],"target":[45],"transported":[47],"average":[48,67],"effect":[50,69],"(TATE)":[51],"and":[52,70,100],"show":[53],"that":[54,113,146],"under":[55],"separable":[57],"assumption,":[60],"TATE":[62],"decomposes":[63],"into":[64],"an":[65],"observed":[66,96],"ratio.":[73],"provide":[75],"two":[76,148],"identification":[77],"strategies":[78,149],"--":[79,99],"one":[80],"using":[81,92],"replicated":[82],"comparing":[84],"same":[86],"treatments":[87,166],"different":[89],"times,":[90],"another":[91],"common":[93,121,155],"arms":[95],"across":[97],"doubly":[102],"robust,":[103],"semiparametrically":[104],"efficient":[105],"estimators":[106,115],"each.":[108],"Monte":[109],"Carlo":[110],"simulations":[111],"confirm":[112],"both":[114],"achieve":[116],"nominal":[117],"coverage,":[118],"with":[119,169],"arm":[122,156],"strategy":[123],"yielding":[124],"substantial":[125],"efficiency":[126],"gains":[127],"when":[128,165],"its":[129],"stronger":[130],"assumptions":[131],"hold.":[132],"apply":[134],"our":[135],"methods":[136],"A/B":[138],"tests":[139],"Upworthy":[142],"Research":[143],"Archive,":[144],"demonstrating":[145],"exhibit":[150],"variance-bias":[152],"tradeoff:":[153],"approach":[157],"offers":[158],"greater":[159],"precision":[160],"may":[162],"incur":[163],"bias":[164],"interact":[167],"heterogeneously":[168],"factors.":[171]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2026-03-11T00:00:00"}
