Randomization Inference for Peer Effects

Many previous causal inference studies require no interference, that is, the potential outcomes of a unit do not depend on the treatments of other units. However, this no-interference assumption becomes unreasonable when a unit interacts with other units in the same group or cluster. In a motivating...

Full description

Saved in:
Bibliographic Details
Published inJournal of the American Statistical Association Vol. 114; no. 528; pp. 1651 - 1664
Main Authors Li, Xinran, Ding, Peng, Lin, Qian, Yang, Dawei, Liu, Jun S.
Format Journal Article
LanguageEnglish
Published Alexandria Taylor & Francis 02.10.2019
Taylor & Francis Group, LLC
Taylor & Francis Ltd
Subjects
Online AccessGet full text
ISSN0162-1459
1537-274X
1537-274X
DOI10.1080/01621459.2018.1512863

Cover

Abstract Many previous causal inference studies require no interference, that is, the potential outcomes of a unit do not depend on the treatments of other units. However, this no-interference assumption becomes unreasonable when a unit interacts with other units in the same group or cluster. In a motivating application, a top Chinese university admits students through two channels: the college entrance exam (also known as Gaokao) and recommendation (often based on Olympiads in various subjects). The university randomly assigns students to dorms, each of which hosts four students. Students within the same dorm live together and have extensive interactions. Therefore, it is likely that peer effects exist and the no-interference assumption does not hold. It is important to understand peer effects, because they give useful guidance for future roommate assignment to improve the performance of students. We define peer effects using potential outcomes. We then propose a randomization-based inference framework to study peer effects with arbitrary numbers of peers and peer types. Our inferential procedure does not assume any parametric model on the outcome distribution. Our analysis gives useful practical guidance for policy makers of the university. Supplementary materials for this article are available online.
AbstractList Many previous causal inference studies require no interference, that is, the potential outcomes of a unit do not depend on the treatments of other units. However, this no-interference assumption becomes unreasonable when a unit interacts with other units in the same group or cluster. In a motivating application, a top Chinese university admits students through two channels: the college entrance exam (also known as Gaokao) and recommendation (often based on Olympiads in various subjects). The university randomly assigns students to dorms, each of which hosts four students. Students within the same dorm live together and have extensive interactions. Therefore, it is likely that peer effects exist and the no-interference assumption does not hold. It is important to understand peer effects, because they give useful guidance for future roommate assignment to improve the performance of students. We define peer effects using potential outcomes. We then propose a randomization-based inference framework to study peer effects with arbitrary numbers of peers and peer types. Our inferential procedure does not assume any parametric model on the outcome distribution. Our analysis gives useful practical guidance for policy makers of the university. Supplementary materials for this article are available online.
Many previous causal inference studies require no interference, that is, the potential outcomes of a unit do not depend on the treatments of other units. However, this no-interference assumption becomes unreasonable when a unit interacts with other units in the same group or cluster. In a motivating application, a top Chinese university admits students through two channels: the college entrance exam (also known as Gaokao) and recommendation (often based on Olympiads in various subjects). The university randomly assigns students to dorms, each of which hosts four students. Students within the same dorm live together and have extensive interactions. Therefore, it is likely that peer effects exist and the no-interference assumption does not hold. It is important to understand peer effects, because they give useful guidance for future roommate assignment to improve the performance of students. We define peer effects using potential outcomes. We then propose a randomization-based inference framework to study peer effects with arbitrary numbers of peers and peer types. Our inferential procedure does not assume any parametric model on the outcome distribution. Our analysis gives useful practical guidance for policy makers of the university. Supplementary materials for this article are available online.
Author Yang, Dawei
Lin, Qian
Li, Xinran
Ding, Peng
Liu, Jun S.
Author_xml – sequence: 1
  givenname: Xinran
  surname: Li
  fullname: Li, Xinran
  organization: Department of Statistics, Harvard University
– sequence: 2
  givenname: Peng
  surname: Ding
  fullname: Ding, Peng
  email: pengdingpku@berkeley.edu
  organization: Department of Statistics, University of California
– sequence: 3
  givenname: Qian
  surname: Lin
  fullname: Lin, Qian
  organization: Center for Statistical Science, Department of Industrial Engineering, Tsinghua University
– sequence: 4
  givenname: Dawei
  surname: Yang
  fullname: Yang, Dawei
  organization: Bureau of Personnel of Chinese Academy of Sciences & School of Education of Peking University, Beijing
– sequence: 5
  givenname: Jun S.
  surname: Liu
  fullname: Liu, Jun S.
  organization: Department of Statistics, Harvard University
BookMark eNqFkMtKAzEUQINUsK1-glAQoZupeU8GN0qpWigoouAupHnAlGlSkylSv94Zp4J0odnczTm5yRmAng_eAnCO4ARBAa8g4hhRVkwwRGKCGMKCkyPQR4zkGc7pWw_0WyZroRMwSGkFm5ML0QfjZ-VNWJefqi6DH829s9F6bUcuxNGTtXE0c87qOp2CY6eqZM_2cwhe72Yv04ds8Xg_n94uMk0JrbOCaMGMgUJwBDllmC-XywJDiI01hnDOCw4potRYbnCBCkcE0YVT2CiqlpoMwbi7dxPD-9amWq7LpG1VKW_DNknMckpQTlHeoBcH6Cpso29eJzEhmDCOWEtdd5SOIaVondRl_f3bOqqykgjKtqL8qSjbinJfsbHZgb2J5VrF3b_eZeetUh3ibwkTmMsmjMCcFA1303Glb4qv1UeIlZG12lUhuqi8LpMkf6_6Ao_wk3U
CitedBy_id crossref_primary_10_1016_j_jeconom_2023_105565
crossref_primary_10_1080_01621459_2022_2027776
crossref_primary_10_1214_24_AOAS1971
crossref_primary_10_15195_v10_a28
crossref_primary_10_2139_ssrn_3802304
crossref_primary_10_3982_ECTA20134
crossref_primary_10_1093_restud_rdae041
crossref_primary_10_1515_jci_2022_0025
crossref_primary_10_1515_jci_2023_0067
crossref_primary_10_1515_ijb_2019_0126
crossref_primary_10_1080_01621459_2023_2284413
crossref_primary_10_1080_01621459_2023_2199814
Cites_doi 10.2307/2298123
10.3386/w16499
10.1016/j.spl.2015.06.011
10.1198/016214506000001112
10.3386/w24003
10.1080/07350015.2013.801251
10.3982/ECTA10168
10.1093/biomet/asw047
10.2307/27917241
10.1111/j.1468-0262.2004.00481.x
10.1111/rssb.12085
10.1198/016214508000000292
10.1080/01621459.2017.1323641
10.1198/016214506000000447
10.1080/01621459.2016.1241178
10.1080/01621459.2017.1295865
10.1007/978-94-007-6094-3_17
10.1093/pan/mps038
10.1017/S0022381611001368
10.1080/01621459.2012.655954
10.1177/0962280210386779
10.1177/0049124112437535
10.1198/016214504000001880
10.1198/jasa.2009.0015
10.1214/12-AOAS583
10.1214/15-AOAS902
10.1214/16-AOAS1005
10.1097/00001648-199503000-00010
10.1097/00001648-199109000-00004
10.1017/S0003055408080039
10.1214/14-STS501
10.1080/01621459.1980.10477517
10.1111/biom.12184
10.1198/016214506000000636
10.1080/01621459.2013.844698
10.1162/00335530151144131
10.1080/01621459.2013.779832
10.1016/j.labeco.2014.05.008
10.1016/S0304-4076(98)00024-4
10.1111/j.1368-423X.2012.00368.x
10.1080/01621459.2016.1194845
10.1093/biomet/asy072
ContentType Journal Article
Copyright 2019 American Statistical Association 2019
Copyright © 2019 American Statistical Association
2019 American Statistical Association
Copyright_xml – notice: 2019 American Statistical Association 2019
– notice: Copyright © 2019 American Statistical Association
– notice: 2019 American Statistical Association
DBID AAYXX
CITATION
8BJ
FQK
JBE
K9.
7S9
L.6
DOI 10.1080/01621459.2018.1512863
DatabaseName CrossRef
International Bibliography of the Social Sciences (IBSS)
International Bibliography of the Social Sciences
International Bibliography of the Social Sciences
ProQuest Health & Medical Complete (Alumni)
AGRICOLA
AGRICOLA - Academic
DatabaseTitle CrossRef
International Bibliography of the Social Sciences (IBSS)
ProQuest Health & Medical Complete (Alumni)
AGRICOLA
AGRICOLA - Academic
DatabaseTitleList AGRICOLA
International Bibliography of the Social Sciences (IBSS)


DeliveryMethod fulltext_linktorsrc
Discipline Statistics
EISSN 1537-274X
EndPage 1664
ExternalDocumentID 10_1080_01621459_2018_1512863
45282639
1512863
Genre Research Article
GroupedDBID -DZ
-~X
..I
.7F
.QJ
0BK
0R~
29L
2AX
30N
4.4
5GY
5RE
692
7WY
85S
8FL
AAAVZ
AABCJ
AAENE
AAGDL
AAHBH
AAHIA
AAJMT
AALDU
AAMIU
AAPUL
AAQRR
ABBHK
ABCCY
ABEHJ
ABFAN
ABFIM
ABJNI
ABLIJ
ABLJU
ABPAQ
ABPEM
ABPFR
ABPPZ
ABRLO
ABTAI
ABUFD
ABXUL
ABXYU
ABYWD
ACGFO
ACGFS
ACGOD
ACIWK
ACMTB
ACNCT
ACTIO
ACTMH
ACUBG
ADCVX
ADGTB
ADLSF
ADMHG
ADODI
ADXHL
AEISY
AENEX
AEOZL
AEPSL
AEUPB
AEYOC
AFFNX
AFRVT
AFVYC
AFXHP
AGDLA
AGMYJ
AHDZW
AIJEM
AIYEW
AKBVH
AKOOK
ALMA_UNASSIGNED_HOLDINGS
ALQZU
ALRMG
AMVHM
AQRUH
AQTUD
AVBZW
AWYRJ
BLEHA
CCCUG
CJ0
CS3
D0L
DGEBU
DKSSO
DQDLB
DSRWC
DU5
EBS
ECEWR
E~A
E~B
F5P
FJW
GTTXZ
H13
HF~
HQ6
HZ~
H~9
H~P
IPNFZ
IPSME
J.P
JAAYA
JAS
JBMMH
JBZCM
JENOY
JHFFW
JKQEH
JLEZI
JLXEF
JMS
JPL
JST
K60
K6~
KYCEM
LJTGL
LU7
M4Z
MS~
MW2
NA5
NY~
O9-
OFU
OK1
P2P
RIG
RNANH
ROSJB
RTWRZ
RWL
RXW
S-T
SA0
SNACF
TAE
TASJS
TBQAZ
TDBHL
TEJ
TFL
TFT
TFW
TN5
TOXWX
TTHFI
TUROJ
U5U
UPT
UT5
UU3
WH7
WZA
YQT
YYM
ZGOLN
~S~
ADYSH
AFSUE
ALIPV
AMPGV
AAYXX
CITATION
8BJ
FQK
JBE
K9.
7S9
L.6
ID FETCH-LOGICAL-c434t-93c85dd08861064526bbb92002dedd36669604144de6d2919f383c9fa2da4abc3
ISSN 0162-1459
1537-274X
IngestDate Fri Oct 03 00:01:21 EDT 2025
Mon Oct 06 18:15:20 EDT 2025
Thu Apr 24 23:12:50 EDT 2025
Wed Oct 01 03:21:31 EDT 2025
Thu May 29 08:47:49 EDT 2025
Mon Oct 20 23:47:26 EDT 2025
IsPeerReviewed true
IsScholarly true
Issue 528
Language English
LinkModel OpenURL
MergedId FETCHMERGED-LOGICAL-c434t-93c85dd08861064526bbb92002dedd36669604144de6d2919f383c9fa2da4abc3
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
PQID 2332356157
PQPubID 41715
PageCount 14
ParticipantIDs crossref_citationtrail_10_1080_01621459_2018_1512863
crossref_primary_10_1080_01621459_2018_1512863
informaworld_taylorfrancis_310_1080_01621459_2018_1512863
jstor_primary_10_2307_45282639
proquest_journals_2332356157
proquest_miscellaneous_2574317417
ProviderPackageCode CITATION
AAYXX
PublicationCentury 2000
PublicationDate 2019-10-02
PublicationDateYYYYMMDD 2019-10-02
PublicationDate_xml – month: 10
  year: 2019
  text: 2019-10-02
  day: 02
PublicationDecade 2010
PublicationPlace Alexandria
PublicationPlace_xml – name: Alexandria
PublicationTitle Journal of the American Statistical Association
PublicationYear 2019
Publisher Taylor & Francis
Taylor & Francis Group, LLC
Taylor & Francis Ltd
Publisher_xml – name: Taylor & Francis
– name: Taylor & Francis Group, LLC
– name: Taylor & Francis Ltd
References e_1_3_3_50_1
Sims C. A (e_1_3_3_46_1) 2006
e_1_3_3_39_1
e_1_3_3_14_1
e_1_3_3_37_1
Forastiere L. (e_1_3_3_18_1) 2016; 1609
e_1_3_3_16_1
e_1_3_3_35_1
e_1_3_3_10_1
e_1_3_3_33_1
e_1_3_3_12_1
e_1_3_3_31_1
e_1_3_3_40_1
Sävje F. (e_1_3_3_45_1) 2017; 1711
An W (e_1_3_3_3_1) 2011
e_1_3_3_7_1
e_1_3_3_9_1
e_1_3_3_29_1
e_1_3_3_25_1
e_1_3_3_48_1
e_1_3_3_27_1
e_1_3_3_21_1
e_1_3_3_44_1
e_1_3_3_5_1
e_1_3_3_23_1
e_1_3_3_42_1
Fisher R. A (e_1_3_3_17_1) 1935
e_1_3_3_30_1
e_1_3_3_51_1
Cox D. R (e_1_3_3_15_1) 1958
e_1_3_3_19_1
e_1_3_3_13_1
e_1_3_3_38_1
e_1_3_3_36_1
e_1_3_3_34_1
e_1_3_3_11_1
e_1_3_3_32_1
e_1_3_3_41_1
e_1_3_3_6_1
e_1_3_3_8_1
e_1_3_3_28_1
e_1_3_3_24_1
e_1_3_3_49_1
e_1_3_3_26_1
e_1_3_3_47_1
e_1_3_3_2_1
e_1_3_3_20_1
e_1_3_3_4_1
e_1_3_3_22_1
e_1_3_3_43_1
References_xml – volume: 1609
  start-page: 06245
  year: 2016
  ident: e_1_3_3_18_1
  article-title: “Identification and Estimation of Treatment and Interference effects in Observational Studies on Networks,”
  publication-title: arXiv
– ident: e_1_3_3_33_1
  doi: 10.2307/2298123
– ident: e_1_3_3_20_1
  doi: 10.3386/w16499
– ident: e_1_3_3_40_1
  doi: 10.1016/j.spl.2015.06.011
– ident: e_1_3_3_41_1
  doi: 10.1198/016214506000001112
– ident: e_1_3_3_2_1
  doi: 10.3386/w24003
– ident: e_1_3_3_19_1
  doi: 10.1080/07350015.2013.801251
– ident: e_1_3_3_13_1
  doi: 10.3982/ECTA10168
– ident: e_1_3_3_31_1
  doi: 10.1093/biomet/asw047
– ident: e_1_3_3_27_1
  doi: 10.2307/27917241
– start-page: 515
  volume-title: The Sage Handbook of Social Network Analysis
  year: 2011
  ident: e_1_3_3_3_1
– ident: e_1_3_3_35_1
  doi: 10.1111/j.1468-0262.2004.00481.x
– volume-title: Planning of Experiments
  year: 1958
  ident: e_1_3_3_15_1
– ident: e_1_3_3_16_1
  doi: 10.1111/rssb.12085
– ident: e_1_3_3_24_1
  doi: 10.1198/016214508000000292
– ident: e_1_3_3_9_1
  doi: 10.1080/01621459.2017.1323641
– ident: e_1_3_3_23_1
  doi: 10.1198/016214506000000447
– ident: e_1_3_3_49_1
– ident: e_1_3_3_8_1
  doi: 10.1080/01621459.2016.1241178
– ident: e_1_3_3_28_1
  doi: 10.1080/01621459.2017.1295865
– volume: 1711
  start-page: 06399
  year: 2017
  ident: e_1_3_3_45_1
  article-title: “Average Treatment Effects in the Presence of Unknown Interference,”
  publication-title: arXiv
– ident: e_1_3_3_50_1
  doi: 10.1007/978-94-007-6094-3_17
– ident: e_1_3_3_12_1
  doi: 10.1093/pan/mps038
– ident: e_1_3_3_25_1
  doi: 10.1017/S0022381611001368
– ident: e_1_3_3_32_1
  doi: 10.1080/01621459.2012.655954
– ident: e_1_3_3_48_1
  doi: 10.1177/0962280210386779
– ident: e_1_3_3_5_1
  doi: 10.1177/0049124112437535
– ident: e_1_3_3_43_1
  doi: 10.1198/016214504000001880
– ident: e_1_3_3_11_1
  doi: 10.1198/jasa.2009.0015
– ident: e_1_3_3_29_1
  doi: 10.1214/12-AOAS583
– ident: e_1_3_3_36_1
– ident: e_1_3_3_7_1
  doi: 10.1214/15-AOAS902
– ident: e_1_3_3_6_1
  doi: 10.1214/16-AOAS1005
– ident: e_1_3_3_22_1
  doi: 10.1097/00001648-199503000-00010
– ident: e_1_3_3_21_1
  doi: 10.1097/00001648-199109000-00004
– ident: e_1_3_3_37_1
  doi: 10.1017/S0003055408080039
– ident: e_1_3_3_38_1
  doi: 10.1214/14-STS501
– ident: e_1_3_3_42_1
  doi: 10.1080/01621459.1980.10477517
– ident: e_1_3_3_39_1
  doi: 10.1111/biom.12184
– ident: e_1_3_3_47_1
  doi: 10.1198/016214506000000636
– ident: e_1_3_3_30_1
  doi: 10.1080/01621459.2013.844698
– ident: e_1_3_3_44_1
  doi: 10.1162/00335530151144131
– volume-title: The Design of Experiments
  year: 1935
  ident: e_1_3_3_17_1
– ident: e_1_3_3_51_1
  doi: 10.1080/01621459.2013.779832
– ident: e_1_3_3_4_1
  doi: 10.1016/j.labeco.2014.05.008
– ident: e_1_3_3_26_1
  doi: 10.1016/S0304-4076(98)00024-4
– ident: e_1_3_3_34_1
  doi: 10.1111/j.1368-423X.2012.00368.x
– ident: e_1_3_3_14_1
  doi: 10.1080/01621459.2016.1194845
– volume-title: Technical Report
  year: 2006
  ident: e_1_3_3_46_1
– ident: e_1_3_3_10_1
  doi: 10.1093/biomet/asy072
SSID ssj0000788
Score 2.5531511
Snippet Many previous causal inference studies require no interference, that is, the potential outcomes of a unit do not depend on the treatments of other units....
SourceID proquest
crossref
jstor
informaworld
SourceType Aggregation Database
Enrichment Source
Index Database
Publisher
StartPage 1651
SubjectTerms academic achievement
Americans
Causal inference
College students
Colleges & universities
Design-based inference
Grade point average (GPA)
hosts
Inference
Interference
issues and policy
Optimal treatment assignment
peers
Performance enhancement
Policy making
Randomization
Regression analysis
Spillover effect
Statistical methods
Statistics
Students
Theory and Methods
universities
Title Randomization Inference for Peer Effects
URI https://www.tandfonline.com/doi/abs/10.1080/01621459.2018.1512863
https://www.jstor.org/stable/45282639
https://www.proquest.com/docview/2332356157
https://www.proquest.com/docview/2574317417
Volume 114
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
journalDatabaseRights – providerCode: PRVEBS
  databaseName: Mathematics Source
  customDbUrl:
  eissn: 1537-274X
  dateEnd: 20241102
  omitProxy: false
  ssIdentifier: ssj0000788
  issn: 0162-1459
  databaseCode: AMVHM
  dateStart: 20121201
  isFulltext: true
  titleUrlDefault: https://www.ebsco.com/products/research-databases/mathematics-source
  providerName: EBSCOhost
– providerCode: PRVLSH
  databaseName: aylor and Francis Online
  customDbUrl:
  mediaType: online
  eissn: 1537-274X
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000788
  issn: 0162-1459
  databaseCode: AHDZW
  dateStart: 19970301
  isFulltext: true
  providerName: Library Specific Holdings
– providerCode: PRVAWR
  databaseName: Taylor & Francis Science and Technology Library-DRAA
  customDbUrl:
  eissn: 1537-274X
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000788
  issn: 0162-1459
  databaseCode: 30N
  dateStart: 19970101
  isFulltext: true
  titleUrlDefault: http://www.tandfonline.com/page/title-lists
  providerName: Taylor & Francis
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwELagXHpB5SWWlipIHLhkRfxI4iOioAqJqqCttJwiO7alSpBFbSokfj0zzuSlXVQel2g3seNoZjyesWe-YexlUNoURuepdL5IpaxtqosA091xMM65CjZgcvLHs_z0Qn5Yq_UYyhuzS1q7rH_uzCv5F67CPeArZsn-BWeHl8IN-A38hStwGK5_xOPPpnGbb5RJCVO9x4zF0MFzj-EbXbTGbyzQSVZJrOPbRtDmHTzDgJ146r--bK5GcTqheijnnpa_2C6qsU8TqftCW9In5oe_nO4yZDrGq40-6Wqr4Md0TzLnaSYJ2Nv3erRIweFdzxRtly5KEqUoKbxTnFlOuLOe_nbY5lsKniIiYUgcEUPzyiUaLSWpyTl2Nj25y-5xUPpY2UO8PhsX6iKWJR2-v0_wQuj1XQPMTJcZsG0fzLq1oEcrZXXA7hNzkzedrDxgd3zzkO0PvL1-xF7NhCYZhCaBcRIUmoSE5jG7eP9u9fY0pXIZaS2FbFMt6lI5B8sGmMR4Xp1bazUG4TjvnAA_FYF4wIF2PndcZzqIUtQ6GO6MNLYWT9hes2n8U5bkQeYWdD_XQIVSWqM9186roGVQRTALJntKVDVhyWNJk69V1kPOEgErJGBFBFyw5dDtewemclsHPSVz1UYxDJ0EVuKWvseRJ9ORMOuhAsqATy30gh31zKpoUl9DC8EF-BSqWLAXw2NQuXiOZhq_uYE2KprdMiue_cf3HbL9caIdsb326sY_BwO3tcdRTH8Ba6aXaQ
linkProvider Taylor & Francis
linkToHtml http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LT8MwDLZgHODCGzEeo0gcuHSwJmmXI0JMG2wTQkPiFjVNcgE2tHUXfj12H2MDIQ47t24bp3Y-J_ZngAsnZBzFMvS5sZHPeaJ9GTk0dxMgOA-E046Kk3v9sP3M71_Ey1wtDKVVUgztcqKIzFeTcdNmdJkSd4UwhQi2qc6k0azTmtUM2SqsCQT71MWAXfe_vXGU9Z4kEZ9kyiqevx6zsD4tsJeWGYu_vHa2FLW2ICkHkWegvNanqa4nnz_4HZcb5TZsFkjVu8l_rR1YscNd2CBwmnM778HlEz589F5UcnqdsnbQw9F4j9aOvZwbebIPz627wW3bLzov-AlnPPUlS5rCGPRAiK7o6DPUWkvK5zDWGIYhD3G6YCxmbGgC2ZAOA91EujgwMY91wg6gMhwN7SF4oeOhRjcSSBxMk-tY2kAaK5zkTkQurgIv9a2SgpacumO8qUbJXlroQZEeVKGHKtRnYh85L8d_AnJ-MlWabYi4vHuJYv_I1rKZn38TJdAr1AyGZ0xW4aT8JVThAyZ4BwsYwlMRVeF8dhmtl45k4qEdTfEekSE43oiOlvi-M1hvD3pd1e30H45hAy_JLN8wOIFKOp7aU8RNqa5lhvEF3RkEFQ
linkToPdf http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LT8MwDLZgSIgLb8RgQJE4cOlgTdouRwRMG48JISZxi5omuQDbtHUXfj1224wNhDjs3DitndixG_szwJkNRRInIvK5NrHPeap8EVtUdx2gcx6EVlkqTn7sRu0ev3sNXTbhuEyrpBjaFkARua0m5R5q6zLiLtBLIXxtKjNpNOt0ZDUjtgwrEd2KURXHZffbGMd560ki8YnGFfH8Nc3c8TQHXuoSFn8Z7fwkam2AcjwUCShv9Umm6unnD3jHhZjchPXST_Wuio21BUumvw1r5JoWyM47cP6Mcw8-yjpOr-MqBz1kxnsyZuQVyMjjXei1bl-u237Zd8FPOeOZL1jaDLVG-4O-FV18RkopQdkc2mjNMOAhRBeMxLSJdCAawmKYmwqbBDrhiUrZHlT6g77ZBy-yPFJoRAKBzDS5SoQJhDahFdyGsU2qwJ24ZVqCklNvjHfZcNilpRwkyUGWcqhCfUo2LFA5_iMQs2sps_x3iC16l0j2D-1xvvCzb6L0eYmSweCMiSrU3I6QpQUY4wgWMHROw7gKp9PHqLt0IZP0zWCCY8Lcf-ON-GCB7zuB1aeblnzodO8PYQ2fiDzZMKhBJRtNzBE6TZk6ztXiC41eArk
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=Randomization+Inference+for+Peer+Effects&rft.jtitle=Journal+of+the+American+Statistical+Association&rft.au=Li%2C+Xinran&rft.au=Ding%2C+Peng&rft.au=Lin%2C+Qian&rft.au=Yang%2C+Dawei&rft.date=2019-10-02&rft.pub=Taylor+%26+Francis&rft.issn=0162-1459&rft.eissn=1537-274X&rft.volume=114&rft.issue=528&rft.spage=1651&rft.epage=1664&rft_id=info:doi/10.1080%2F01621459.2018.1512863&rft.externalDocID=1512863
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0162-1459&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0162-1459&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0162-1459&client=summon