Data-Driven Switchback Experiments: Theoretical Tradeoffs and Empirical Bayes Designs
We study the design and analysis of switchback experiments conducted on a single aggregate unit. The design problem is to partition the continuous time space into intervals and switch treatments between intervals, in order to minimize the estimation error of the treatment effect. We show that the es...
Saved in:
Published in | arXiv.org |
---|---|
Main Authors | , , |
Format | Paper |
Language | English |
Published |
Ithaca
Cornell University Library, arXiv.org
10.06.2024
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Abstract | We study the design and analysis of switchback experiments conducted on a single aggregate unit. The design problem is to partition the continuous time space into intervals and switch treatments between intervals, in order to minimize the estimation error of the treatment effect. We show that the estimation error depends on four factors: carryover effects, periodicity, serially correlated outcomes, and impacts from simultaneous experiments. We derive a rigorous bias-variance decomposition and show the tradeoffs of the estimation error from these factors. The decomposition provides three new insights in choosing a design: First, balancing the periodicity between treated and control intervals reduces the variance; second, switching less frequently reduces the bias from carryover effects while increasing the variance from correlated outcomes, and vice versa; third, randomizing interval start and end points reduces both bias and variance from simultaneous experiments. Combining these insights, we propose a new empirical Bayes design approach. This approach uses prior data and experiments for designing future experiments. We illustrate this approach using real data from a ride-sharing platform, yielding a design that reduces MSE by 33% compared to the status quo design used on the platform. |
---|---|
AbstractList | We study the design and analysis of switchback experiments conducted on a single aggregate unit. The design problem is to partition the continuous time space into intervals and switch treatments between intervals, in order to minimize the estimation error of the treatment effect. We show that the estimation error depends on four factors: carryover effects, periodicity, serially correlated outcomes, and impacts from simultaneous experiments. We derive a rigorous bias-variance decomposition and show the tradeoffs of the estimation error from these factors. The decomposition provides three new insights in choosing a design: First, balancing the periodicity between treated and control intervals reduces the variance; second, switching less frequently reduces the bias from carryover effects while increasing the variance from correlated outcomes, and vice versa; third, randomizing interval start and end points reduces both bias and variance from simultaneous experiments. Combining these insights, we propose a new empirical Bayes design approach. This approach uses prior data and experiments for designing future experiments. We illustrate this approach using real data from a ride-sharing platform, yielding a design that reduces MSE by 33% compared to the status quo design used on the platform. |
Author | Chin, Alex Taylor, Sean J Xiong, Ruoxuan |
Author_xml | – sequence: 1 givenname: Ruoxuan surname: Xiong fullname: Xiong, Ruoxuan – sequence: 2 givenname: Alex surname: Chin fullname: Chin, Alex – sequence: 3 givenname: Sean surname: Taylor middlename: J fullname: Taylor, Sean J |
BookMark | eNqNiksOgjAUABujiT_u0MQ1SW0F1KWCcS-uSYWHFKHFvvq7vcZ4AFeTzMyY9LXR0CMjLsTcXy44HxIPsWaM8TDiQSBG5BhLJ_3Yqjtoengol1cnmV9o8uzAqha0wzVNKzAWnMplQ1MrCzBliVTqgiZtp-zXb-QLkMaA6qxxSgalbBC8HydktkvS7d7vrLneAF1Wm5vVn5QJFkZsLlY8EP9dbx1FQlw |
ContentType | Paper |
Copyright | 2024. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
Copyright_xml | – notice: 2024. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
DBID | 8FE 8FG ABJCF ABUWG AFKRA AZQEC BENPR BGLVJ CCPQU DWQXO HCIFZ L6V M7S PIMPY PQEST PQQKQ PQUKI PRINS PTHSS |
DatabaseName | ProQuest SciTech Collection ProQuest Technology Collection Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest Central ProQuest Central Essentials AUTh Library subscriptions: ProQuest Central Technology Collection ProQuest One Community College ProQuest Central SciTech Premium Collection ProQuest Engineering Collection Engineering Database Publicly Available Content Database ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China Engineering Collection |
DatabaseTitle | Publicly Available Content Database Engineering Database Technology Collection ProQuest Central Essentials ProQuest One Academic Eastern Edition ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Technology Collection ProQuest SciTech Collection ProQuest Central China ProQuest Central ProQuest Engineering Collection ProQuest One Academic UKI Edition ProQuest Central Korea Materials Science & Engineering Collection ProQuest One Academic Engineering Collection |
DatabaseTitleList | Publicly Available Content Database |
Database_xml | – sequence: 1 dbid: 8FG name: ProQuest Technology Collection url: https://search.proquest.com/technologycollection1 sourceTypes: Aggregation Database |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Physics |
EISSN | 2331-8422 |
Genre | Working Paper/Pre-Print |
GroupedDBID | 8FE 8FG ABJCF ABUWG AFKRA ALMA_UNASSIGNED_HOLDINGS AZQEC BENPR BGLVJ CCPQU DWQXO FRJ HCIFZ L6V M7S M~E PIMPY PQEST PQQKQ PQUKI PRINS PTHSS |
ID | FETCH-proquest_journals_30670139253 |
IEDL.DBID | 8FG |
IngestDate | Thu Oct 10 17:32:06 EDT 2024 |
IsOpenAccess | true |
IsPeerReviewed | false |
IsScholarly | false |
Language | English |
LinkModel | DirectLink |
MergedId | FETCHMERGED-proquest_journals_30670139253 |
OpenAccessLink | https://www.proquest.com/docview/3067013925?pq-origsite=%requestingapplication% |
PQID | 3067013925 |
PQPubID | 2050157 |
ParticipantIDs | proquest_journals_3067013925 |
PublicationCentury | 2000 |
PublicationDate | 20240610 |
PublicationDateYYYYMMDD | 2024-06-10 |
PublicationDate_xml | – month: 06 year: 2024 text: 20240610 day: 10 |
PublicationDecade | 2020 |
PublicationPlace | Ithaca |
PublicationPlace_xml | – name: Ithaca |
PublicationTitle | arXiv.org |
PublicationYear | 2024 |
Publisher | Cornell University Library, arXiv.org |
Publisher_xml | – name: Cornell University Library, arXiv.org |
SSID | ssj0002672553 |
Score | 3.5527623 |
SecondaryResourceType | preprint |
Snippet | We study the design and analysis of switchback experiments conducted on a single aggregate unit. The design problem is to partition the continuous time space... |
SourceID | proquest |
SourceType | Aggregation Database |
SubjectTerms | Bias Decomposition Design Errors Experiments Intervals Tradeoffs |
Title | Data-Driven Switchback Experiments: Theoretical Tradeoffs and Empirical Bayes Designs |
URI | https://www.proquest.com/docview/3067013925 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1LSwMxEB60i-CtvlBbS0CvwX2mqxeh7q5FaCnaQm8lm2ShiHbdrIgXf7uZsG0PQo9hICRDmFe--QbgRiLpduEHtO-Gioa59ChnUUil5K7woljElqd7NGbDWfg8j-ZNwU03sMq1TbSGWq4E1shvMbTFcMWPHspPilOj8He1GaGxD46HTHjYKZ49bWosPuubiDn4Z2at78ja4Ex4qaoj2FMfx3BgIZdCn8As4TWnSYXmhrx-L436ci7eSLrh3Nf3ZLrtMyTGrUi1KgpNTPZP0vdyaek9yID_KE0SC8XQp3CdpdPHIV2fZdG8Fr3Y3i04g5ZJ-9U5EBHnksmYC4RMGtGdKBSTXDBUn5FcQHfXTpe7xR049I17RtCT53ahVVdf6sq41zrvWR32wBmk48mLWY1-0z_Z1IYU |
link.rule.ids | 786,790,12792,21416,33406,33777,43633,43838 |
linkProvider | ProQuest |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV3fS8MwED60RfTNn_hjakBfg23XZtUXYbaj6laGdrC3kiYpjKGrTUX8701Ctz0Iez4IyRHuu7t8-Q7glmvR7dLr4p7jC-wX3MWUBD7mnDrMDUIWGp3uUUqSif8yDaZtw022tMplTDSBmi-Y7pHf6dRWpyte8Fh9YT01Sr-utiM0tsHWkpuhBXY_Tsdvqy6LR3oqZ-7-C7QGPQb7YI9pJeoD2BKfh7BjSJdMHsEkog3FUa0DDnr_mSkHFpTNUbxS3ZcPKFv_NEQKWLhYlKVEqv5H8Uc1MwIfqE9_hUSRIWPIY7gZxNlTgpd7ydv7IvP16bonYKnCX5wCYmHBCQ8p06RJZbpnpSCcMqIdqCxn0Nm00vlm8zXsJtlomA-f09cL2PMUWGsKlOt0wGrqb3GpwLYprlqP_gEv7Ieg |
openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Data-Driven+Switchback+Experiments%3A+Theoretical+Tradeoffs+and+Empirical+Bayes+Designs&rft.jtitle=arXiv.org&rft.au=Xiong%2C+Ruoxuan&rft.au=Chin%2C+Alex&rft.au=Taylor%2C+Sean+J&rft.date=2024-06-10&rft.pub=Cornell+University+Library%2C+arXiv.org&rft.eissn=2331-8422 |