Regression Discontinuity Design under Self-selection Article Swipe
Sida Peng
,
Yang Ning
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1911.09248
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1911.09248
In Regression Discontinuity (RD) design, self-selection leads to different distributions of covariates on two sides of the policy intervention, which essentially violates the continuity of potential outcome assumption. The standard RD estimand becomes difficult to interpret due to the existence of some indirect effect, i.e. the effect due to self selection. We show that the direct causal effect of interest can still be recovered under a class of estimands. Specifically, we consider a class of weighted average treatment effects tailored for potentially different target populations. We show that a special case of our estimands can recover the average treatment effect under the conditional independence assumption per Angrist and Rokkanen (2015), and another example is the estimand recently proposed in Frölich and Huber (2018). We propose a set of estimators through a weighted local linear regression framework and prove the consistency and asymptotic normality of the estimators. Our approach can be further extended to the fuzzy RD case. In simulation exercises, we compare the performance of our estimator with the standard RD estimator. Finally, we apply our method to two empirical data sets: the U.S. House elections data in Lee (2008) and a novel data set from Microsoft Bing on Generalized Second Price (GSP) auction.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1911.09248
- https://arxiv.org/pdf/1911.09248
- OA Status
- green
- Cited By
- 1
- References
- 15
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2989971270
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2989971270Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.1911.09248Digital Object Identifier
- Title
-
Regression Discontinuity Design under Self-selectionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-11-21Full publication date if available
- Authors
-
Sida Peng, Yang NingList of authors in order
- Landing page
-
https://arxiv.org/abs/1911.09248Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/1911.09248Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/1911.09248Direct OA link when available
- Concepts
-
Estimator, Regression discontinuity design, Covariate, Econometrics, Average treatment effect, Consistency (knowledge bases), Asymptotic distribution, Mathematics, Computer science, Statistics, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 1Per-year citation counts (last 5 years)
- References (count)
-
15Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W2989971270 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.1911.09248 |
| ids.doi | https://doi.org/10.48550/arxiv.1911.09248 |
| ids.mag | 2989971270 |
| ids.openalex | https://openalex.org/W2989971270 |
| fwci | |
| type | preprint |
| title | Regression Discontinuity Design under Self-selection |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10845 |
| topics[0].field.id | https://openalex.org/fields/26 |
| topics[0].field.display_name | Mathematics |
| topics[0].score | 0.9994000196456909 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2613 |
| topics[0].subfield.display_name | Statistics and Probability |
| topics[0].display_name | Advanced Causal Inference Techniques |
| topics[1].id | https://openalex.org/T11161 |
| topics[1].field.id | https://openalex.org/fields/14 |
| topics[1].field.display_name | Business, Management and Accounting |
| topics[1].score | 0.9951000213623047 |
| topics[1].domain.id | https://openalex.org/domains/2 |
| topics[1].domain.display_name | Social Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1406 |
| topics[1].subfield.display_name | Marketing |
| topics[1].display_name | Consumer Market Behavior and Pricing |
| topics[2].id | https://openalex.org/T10136 |
| topics[2].field.id | https://openalex.org/fields/26 |
| topics[2].field.display_name | Mathematics |
| topics[2].score | 0.9939000010490417 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2613 |
| topics[2].subfield.display_name | Statistics and Probability |
| topics[2].display_name | Statistical Methods and Inference |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C185429906 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7646210193634033 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q1130160 |
| concepts[0].display_name | Estimator |
| concepts[1].id | https://openalex.org/C66912227 |
| concepts[1].level | 2 |
| concepts[1].score | 0.675356388092041 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q2138712 |
| concepts[1].display_name | Regression discontinuity design |
| concepts[2].id | https://openalex.org/C119043178 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5830709934234619 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q320723 |
| concepts[2].display_name | Covariate |
| concepts[3].id | https://openalex.org/C149782125 |
| concepts[3].level | 1 |
| concepts[3].score | 0.5801887512207031 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q160039 |
| concepts[3].display_name | Econometrics |
| concepts[4].id | https://openalex.org/C89337504 |
| concepts[4].level | 3 |
| concepts[4].score | 0.5586214065551758 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q4828276 |
| concepts[4].display_name | Average treatment effect |
| concepts[5].id | https://openalex.org/C2776436953 |
| concepts[5].level | 2 |
| concepts[5].score | 0.4570482075214386 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q5163215 |
| concepts[5].display_name | Consistency (knowledge bases) |
| concepts[6].id | https://openalex.org/C65778772 |
| concepts[6].level | 3 |
| concepts[6].score | 0.4464796483516693 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q12345341 |
| concepts[6].display_name | Asymptotic distribution |
| concepts[7].id | https://openalex.org/C33923547 |
| concepts[7].level | 0 |
| concepts[7].score | 0.4306586682796478 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[7].display_name | Mathematics |
| concepts[8].id | https://openalex.org/C41008148 |
| concepts[8].level | 0 |
| concepts[8].score | 0.3715055584907532 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[8].display_name | Computer science |
| concepts[9].id | https://openalex.org/C105795698 |
| concepts[9].level | 1 |
| concepts[9].score | 0.3708306550979614 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[9].display_name | Statistics |
| concepts[10].id | https://openalex.org/C154945302 |
| concepts[10].level | 1 |
| concepts[10].score | 0.0980299711227417 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[10].display_name | Artificial intelligence |
| keywords[0].id | https://openalex.org/keywords/estimator |
| keywords[0].score | 0.7646210193634033 |
| keywords[0].display_name | Estimator |
| keywords[1].id | https://openalex.org/keywords/regression-discontinuity-design |
| keywords[1].score | 0.675356388092041 |
| keywords[1].display_name | Regression discontinuity design |
| keywords[2].id | https://openalex.org/keywords/covariate |
| keywords[2].score | 0.5830709934234619 |
| keywords[2].display_name | Covariate |
| keywords[3].id | https://openalex.org/keywords/econometrics |
| keywords[3].score | 0.5801887512207031 |
| keywords[3].display_name | Econometrics |
| keywords[4].id | https://openalex.org/keywords/average-treatment-effect |
| keywords[4].score | 0.5586214065551758 |
| keywords[4].display_name | Average treatment effect |
| keywords[5].id | https://openalex.org/keywords/consistency |
| keywords[5].score | 0.4570482075214386 |
| keywords[5].display_name | Consistency (knowledge bases) |
| keywords[6].id | https://openalex.org/keywords/asymptotic-distribution |
| keywords[6].score | 0.4464796483516693 |
| keywords[6].display_name | Asymptotic distribution |
| keywords[7].id | https://openalex.org/keywords/mathematics |
| keywords[7].score | 0.4306586682796478 |
| keywords[7].display_name | Mathematics |
| keywords[8].id | https://openalex.org/keywords/computer-science |
| keywords[8].score | 0.3715055584907532 |
| keywords[8].display_name | Computer science |
| keywords[9].id | https://openalex.org/keywords/statistics |
| keywords[9].score | 0.3708306550979614 |
| keywords[9].display_name | Statistics |
| keywords[10].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[10].score | 0.0980299711227417 |
| keywords[10].display_name | Artificial intelligence |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:1911.09248 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400194 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | arXiv (Cornell University) |
| locations[0].source.host_organization | https://openalex.org/I205783295 |
| locations[0].source.host_organization_name | Cornell University |
| locations[0].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[0].license | |
| locations[0].pdf_url | https://arxiv.org/pdf/1911.09248 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/1911.09248 |
| locations[1].id | doi:10.48550/arxiv.1911.09248 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | cc-by |
| locations[1].pdf_url | |
| locations[1].version | |
| locations[1].raw_type | article |
| locations[1].license_id | https://openalex.org/licenses/cc-by |
| locations[1].is_accepted | False |
| locations[1].is_published | |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://doi.org/10.48550/arxiv.1911.09248 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5073087748 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-6546-4525 |
| authorships[0].author.display_name | Sida Peng |
| authorships[0].countries | GB |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I4210164937 |
| authorships[0].affiliations[0].raw_affiliation_string | (Microsoft) |
| authorships[0].institutions[0].id | https://openalex.org/I4210164937 |
| authorships[0].institutions[0].ror | https://ror.org/05k87vq12 |
| authorships[0].institutions[0].type | company |
| authorships[0].institutions[0].lineage | https://openalex.org/I1290206253, https://openalex.org/I4210164937 |
| authorships[0].institutions[0].country_code | GB |
| authorships[0].institutions[0].display_name | Microsoft Research (United Kingdom) |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Sida Peng |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | (Microsoft) |
| authorships[1].author.id | https://openalex.org/A5070406375 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-6877-9231 |
| authorships[1].author.display_name | Yang Ning |
| authorships[1].countries | US |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I205783295 |
| authorships[1].affiliations[0].raw_affiliation_string | cornell University |
| authorships[1].institutions[0].id | https://openalex.org/I205783295 |
| authorships[1].institutions[0].ror | https://ror.org/05bnh6r87 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I205783295 |
| authorships[1].institutions[0].country_code | US |
| authorships[1].institutions[0].display_name | Cornell University |
| authorships[1].author_position | last |
| authorships[1].raw_author_name | Yang Ning |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | cornell University |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/1911.09248 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Regression Discontinuity Design under Self-selection |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T10845 |
| primary_topic.field.id | https://openalex.org/fields/26 |
| primary_topic.field.display_name | Mathematics |
| primary_topic.score | 0.9994000196456909 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2613 |
| primary_topic.subfield.display_name | Statistics and Probability |
| primary_topic.display_name | Advanced Causal Inference Techniques |
| related_works | https://openalex.org/W4248009324, https://openalex.org/W2011087402, https://openalex.org/W4213439002, https://openalex.org/W2748953975, https://openalex.org/W2949083691, https://openalex.org/W1802854002, https://openalex.org/W2112143119, https://openalex.org/W4300878635, https://openalex.org/W2950643425, https://openalex.org/W3205862111 |
| cited_by_count | 1 |
| counts_by_year[0].year | 2023 |
| counts_by_year[0].cited_by_count | 1 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:1911.09248 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/1911.09248 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/1911.09248 |
| primary_location.id | pmh:oai:arXiv.org:1911.09248 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/1911.09248 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/1911.09248 |
| publication_date | 2019-11-21 |
| publication_year | 2019 |
| referenced_works | https://openalex.org/W2770565603, https://openalex.org/W3121556564, https://openalex.org/W2141019942, https://openalex.org/W2950770909, https://openalex.org/W1597926580, https://openalex.org/W2001947543, https://openalex.org/W3122305203, https://openalex.org/W2114242548, https://openalex.org/W2117935976, https://openalex.org/W3122958054, https://openalex.org/W3125108970, https://openalex.org/W2027244596, https://openalex.org/W157449371, https://openalex.org/W2052160362, https://openalex.org/W2270563227 |
| referenced_works_count | 15 |
| abstract_inverted_index.a | 65, 72, 88, 125, 130, 191 |
| abstract_inverted_index.In | 0, 157 |
| abstract_inverted_index.RD | 30, 155, 170 |
| abstract_inverted_index.We | 51, 85, 123 |
| abstract_inverted_index.be | 62, 149 |
| abstract_inverted_index.in | 118, 187 |
| abstract_inverted_index.is | 113 |
| abstract_inverted_index.of | 10, 15, 24, 40, 58, 67, 74, 91, 127, 143, 164 |
| abstract_inverted_index.on | 12, 198 |
| abstract_inverted_index.to | 7, 34, 37, 48, 152, 177 |
| abstract_inverted_index.we | 70, 160, 173 |
| abstract_inverted_index.Lee | 188 |
| abstract_inverted_index.Our | 146 |
| abstract_inverted_index.The | 28 |
| abstract_inverted_index.and | 107, 110, 120, 136, 140, 190 |
| abstract_inverted_index.can | 60, 94, 148 |
| abstract_inverted_index.due | 36, 47 |
| abstract_inverted_index.for | 80 |
| abstract_inverted_index.our | 92, 165, 175 |
| abstract_inverted_index.per | 105 |
| abstract_inverted_index.set | 126, 194 |
| abstract_inverted_index.the | 16, 22, 38, 45, 54, 96, 101, 114, 138, 144, 153, 162, 168, 182 |
| abstract_inverted_index.two | 13, 178 |
| abstract_inverted_index.(RD) | 3 |
| abstract_inverted_index.Bing | 197 |
| abstract_inverted_index.U.S. | 183 |
| abstract_inverted_index.case | 90 |
| abstract_inverted_index.data | 180, 186, 193 |
| abstract_inverted_index.from | 195 |
| abstract_inverted_index.i.e. | 44 |
| abstract_inverted_index.self | 49 |
| abstract_inverted_index.show | 52, 86 |
| abstract_inverted_index.some | 41 |
| abstract_inverted_index.that | 53, 87 |
| abstract_inverted_index.with | 167 |
| abstract_inverted_index.(GSP) | 202 |
| abstract_inverted_index.House | 184 |
| abstract_inverted_index.Huber | 121 |
| abstract_inverted_index.Price | 201 |
| abstract_inverted_index.apply | 174 |
| abstract_inverted_index.case. | 156 |
| abstract_inverted_index.class | 66, 73 |
| abstract_inverted_index.fuzzy | 154 |
| abstract_inverted_index.leads | 6 |
| abstract_inverted_index.local | 132 |
| abstract_inverted_index.novel | 192 |
| abstract_inverted_index.prove | 137 |
| abstract_inverted_index.sets: | 181 |
| abstract_inverted_index.sides | 14 |
| abstract_inverted_index.still | 61 |
| abstract_inverted_index.under | 64, 100 |
| abstract_inverted_index.which | 19 |
| abstract_inverted_index.(2008) | 189 |
| abstract_inverted_index.Second | 200 |
| abstract_inverted_index.causal | 56 |
| abstract_inverted_index.direct | 55 |
| abstract_inverted_index.effect | 46, 57, 99 |
| abstract_inverted_index.linear | 133 |
| abstract_inverted_index.method | 176 |
| abstract_inverted_index.policy | 17 |
| abstract_inverted_index.target | 83 |
| abstract_inverted_index.(2015), | 109 |
| abstract_inverted_index.(2018). | 122 |
| abstract_inverted_index.Angrist | 106 |
| abstract_inverted_index.another | 111 |
| abstract_inverted_index.average | 76, 97 |
| abstract_inverted_index.becomes | 32 |
| abstract_inverted_index.compare | 161 |
| abstract_inverted_index.design, | 4 |
| abstract_inverted_index.effect, | 43 |
| abstract_inverted_index.effects | 78 |
| abstract_inverted_index.example | 112 |
| abstract_inverted_index.further | 150 |
| abstract_inverted_index.outcome | 26 |
| abstract_inverted_index.propose | 124 |
| abstract_inverted_index.recover | 95 |
| abstract_inverted_index.special | 89 |
| abstract_inverted_index.through | 129 |
| abstract_inverted_index.Finally, | 172 |
| abstract_inverted_index.Frölich | 119 |
| abstract_inverted_index.Rokkanen | 108 |
| abstract_inverted_index.approach | 147 |
| abstract_inverted_index.auction. | 203 |
| abstract_inverted_index.consider | 71 |
| abstract_inverted_index.estimand | 31, 115 |
| abstract_inverted_index.extended | 151 |
| abstract_inverted_index.indirect | 42 |
| abstract_inverted_index.interest | 59 |
| abstract_inverted_index.proposed | 117 |
| abstract_inverted_index.recently | 116 |
| abstract_inverted_index.standard | 29, 169 |
| abstract_inverted_index.tailored | 79 |
| abstract_inverted_index.violates | 21 |
| abstract_inverted_index.weighted | 75, 131 |
| abstract_inverted_index.Microsoft | 196 |
| abstract_inverted_index.different | 8, 82 |
| abstract_inverted_index.difficult | 33 |
| abstract_inverted_index.elections | 185 |
| abstract_inverted_index.empirical | 179 |
| abstract_inverted_index.estimands | 93 |
| abstract_inverted_index.estimator | 166 |
| abstract_inverted_index.existence | 39 |
| abstract_inverted_index.framework | 135 |
| abstract_inverted_index.interpret | 35 |
| abstract_inverted_index.normality | 142 |
| abstract_inverted_index.potential | 25 |
| abstract_inverted_index.recovered | 63 |
| abstract_inverted_index.treatment | 77, 98 |
| abstract_inverted_index.Regression | 1 |
| abstract_inverted_index.assumption | 104 |
| abstract_inverted_index.asymptotic | 141 |
| abstract_inverted_index.continuity | 23 |
| abstract_inverted_index.covariates | 11 |
| abstract_inverted_index.estimands. | 68 |
| abstract_inverted_index.estimator. | 171 |
| abstract_inverted_index.estimators | 128 |
| abstract_inverted_index.exercises, | 159 |
| abstract_inverted_index.regression | 134 |
| abstract_inverted_index.selection. | 50 |
| abstract_inverted_index.simulation | 158 |
| abstract_inverted_index.Generalized | 199 |
| abstract_inverted_index.assumption. | 27 |
| abstract_inverted_index.conditional | 102 |
| abstract_inverted_index.consistency | 139 |
| abstract_inverted_index.essentially | 20 |
| abstract_inverted_index.estimators. | 145 |
| abstract_inverted_index.performance | 163 |
| abstract_inverted_index.potentially | 81 |
| abstract_inverted_index.independence | 103 |
| abstract_inverted_index.populations. | 84 |
| abstract_inverted_index.Discontinuity | 2 |
| abstract_inverted_index.Specifically, | 69 |
| abstract_inverted_index.distributions | 9 |
| abstract_inverted_index.intervention, | 18 |
| abstract_inverted_index.self-selection | 5 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 2 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.5400000214576721 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile |