Doubly Robust Semiparametric Difference-in-Differences Estimators with\n High-Dimensional Data Article Swipe
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2009.03151
This paper proposes a doubly robust two-stage semiparametric\ndifference-in-difference estimator for estimating heterogeneous treatment\neffects with high-dimensional data. Our new estimator is robust to model\nmiss-specifications and allows for, but does not require, many more regressors\nthan observations. The first stage allows a general set of machine learning\nmethods to be used to estimate the propensity score. In the second stage, we\nderive the rates of convergence for both the parametric parameter and the\nunknown function under a partially linear specification for the outcome\nequation. We also provide bias correction procedures to allow for valid\ninference for the heterogeneous treatment effects. We evaluate the finite\nsample performance with extensive simulation studies. Additionally, a real data\nanalysis on the effect of Fair Minimum Wage Act on the unemployment rate is\nperformed as an illustration of our method. An R package for implementing the\nproposed method is available on Github.\n
Related Topics
- Type
- preprint
- Landing Page
- http://arxiv.org/abs/2009.03151
- https://arxiv.org/pdf/2009.03151
- OA Status
- green
- Cited By
- 3
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3124897287
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3124897287Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2009.03151Digital Object Identifier
- Title
-
Doubly Robust Semiparametric Difference-in-Differences Estimators with\n High-Dimensional DataWork title
- Type
-
preprintOpenAlex work type
- Publication year
-
2020Year of publication
- Publication date
-
2020-09-07Full publication date if available
- Authors
-
Yang Ning, Sida Peng, Jing TaoList of authors in order
- Landing page
-
https://arxiv.org/abs/2009.03151Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2009.03151Direct 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/2009.03151Direct OA link when available
- Concepts
-
Estimator, Semiparametric model, Parametric statistics, Computer science, Semiparametric regression, Inference, Data set, Rate of convergence, Econometrics, Mathematical optimization, Mathematics, Statistics, Artificial intelligence, Key (lock), Computer securityTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2022: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W3124897287 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2009.03151 |
| ids.mag | 3124897287 |
| ids.openalex | https://openalex.org/W3124897287 |
| fwci | 0.24593193 |
| type | preprint |
| title | Doubly Robust Semiparametric Difference-in-Differences Estimators with\n High-Dimensional Data |
| 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 | 0.9926000237464905 |
| 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/T10136 |
| topics[1].field.id | https://openalex.org/fields/26 |
| topics[1].field.display_name | Mathematics |
| topics[1].score | 0.9426000118255615 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2613 |
| topics[1].subfield.display_name | Statistics and Probability |
| topics[1].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 | 0.789612889289856 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q1130160 |
| concepts[0].display_name | Estimator |
| concepts[1].id | https://openalex.org/C78297888 |
| concepts[1].level | 3 |
| concepts[1].score | 0.6056051850318909 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q7449607 |
| concepts[1].display_name | Semiparametric model |
| concepts[2].id | https://openalex.org/C117251300 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5946128368377686 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q1849855 |
| concepts[2].display_name | Parametric statistics |
| concepts[3].id | https://openalex.org/C41008148 |
| concepts[3].level | 0 |
| concepts[3].score | 0.569682240486145 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[3].display_name | Computer science |
| concepts[4].id | https://openalex.org/C19539793 |
| concepts[4].level | 3 |
| concepts[4].score | 0.4963989853858948 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q7449609 |
| concepts[4].display_name | Semiparametric regression |
| concepts[5].id | https://openalex.org/C2776214188 |
| concepts[5].level | 2 |
| concepts[5].score | 0.4952951967716217 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q408386 |
| concepts[5].display_name | Inference |
| concepts[6].id | https://openalex.org/C58489278 |
| concepts[6].level | 2 |
| concepts[6].score | 0.4501769244670868 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q1172284 |
| concepts[6].display_name | Data set |
| concepts[7].id | https://openalex.org/C57869625 |
| concepts[7].level | 3 |
| concepts[7].score | 0.41208022832870483 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q1783502 |
| concepts[7].display_name | Rate of convergence |
| concepts[8].id | https://openalex.org/C149782125 |
| concepts[8].level | 1 |
| concepts[8].score | 0.411193311214447 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q160039 |
| concepts[8].display_name | Econometrics |
| concepts[9].id | https://openalex.org/C126255220 |
| concepts[9].level | 1 |
| concepts[9].score | 0.3526065945625305 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q141495 |
| concepts[9].display_name | Mathematical optimization |
| concepts[10].id | https://openalex.org/C33923547 |
| concepts[10].level | 0 |
| concepts[10].score | 0.34765589237213135 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[10].display_name | Mathematics |
| concepts[11].id | https://openalex.org/C105795698 |
| concepts[11].level | 1 |
| concepts[11].score | 0.2923954129219055 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[11].display_name | Statistics |
| concepts[12].id | https://openalex.org/C154945302 |
| concepts[12].level | 1 |
| concepts[12].score | 0.18010401725769043 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[12].display_name | Artificial intelligence |
| concepts[13].id | https://openalex.org/C26517878 |
| concepts[13].level | 2 |
| concepts[13].score | 0.0791735053062439 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q228039 |
| concepts[13].display_name | Key (lock) |
| concepts[14].id | https://openalex.org/C38652104 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q3510521 |
| concepts[14].display_name | Computer security |
| keywords[0].id | https://openalex.org/keywords/estimator |
| keywords[0].score | 0.789612889289856 |
| keywords[0].display_name | Estimator |
| keywords[1].id | https://openalex.org/keywords/semiparametric-model |
| keywords[1].score | 0.6056051850318909 |
| keywords[1].display_name | Semiparametric model |
| keywords[2].id | https://openalex.org/keywords/parametric-statistics |
| keywords[2].score | 0.5946128368377686 |
| keywords[2].display_name | Parametric statistics |
| keywords[3].id | https://openalex.org/keywords/computer-science |
| keywords[3].score | 0.569682240486145 |
| keywords[3].display_name | Computer science |
| keywords[4].id | https://openalex.org/keywords/semiparametric-regression |
| keywords[4].score | 0.4963989853858948 |
| keywords[4].display_name | Semiparametric regression |
| keywords[5].id | https://openalex.org/keywords/inference |
| keywords[5].score | 0.4952951967716217 |
| keywords[5].display_name | Inference |
| keywords[6].id | https://openalex.org/keywords/data-set |
| keywords[6].score | 0.4501769244670868 |
| keywords[6].display_name | Data set |
| keywords[7].id | https://openalex.org/keywords/rate-of-convergence |
| keywords[7].score | 0.41208022832870483 |
| keywords[7].display_name | Rate of convergence |
| keywords[8].id | https://openalex.org/keywords/econometrics |
| keywords[8].score | 0.411193311214447 |
| keywords[8].display_name | Econometrics |
| keywords[9].id | https://openalex.org/keywords/mathematical-optimization |
| keywords[9].score | 0.3526065945625305 |
| keywords[9].display_name | Mathematical optimization |
| keywords[10].id | https://openalex.org/keywords/mathematics |
| keywords[10].score | 0.34765589237213135 |
| keywords[10].display_name | Mathematics |
| keywords[11].id | https://openalex.org/keywords/statistics |
| keywords[11].score | 0.2923954129219055 |
| keywords[11].display_name | Statistics |
| keywords[12].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[12].score | 0.18010401725769043 |
| keywords[12].display_name | Artificial intelligence |
| keywords[13].id | https://openalex.org/keywords/key |
| keywords[13].score | 0.0791735053062439 |
| keywords[13].display_name | Key (lock) |
| language | |
| locations[0].id | pmh:oai:arXiv.org:2009.03151 |
| 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/2009.03151 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | text |
| 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/2009.03151 |
| indexed_in | arxiv |
| authorships[0].author.id | https://openalex.org/A5070406375 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-6877-9231 |
| authorships[0].author.display_name | Yang Ning |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Yang Ning |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5073087748 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-6546-4525 |
| authorships[1].author.display_name | Sida Peng |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Sida Peng |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5101516219 |
| authorships[2].author.orcid | https://orcid.org/0009-0004-6271-4321 |
| authorships[2].author.display_name | Jing Tao |
| authorships[2].author_position | last |
| authorships[2].raw_author_name | Jing Tao |
| authorships[2].is_corresponding | False |
| 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/2009.03151 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2021-02-01T00:00:00 |
| display_name | Doubly Robust Semiparametric Difference-in-Differences Estimators with\n High-Dimensional Data |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| 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 | 0.9926000237464905 |
| 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/W3123217622, https://openalex.org/W3151010055, https://openalex.org/W1532780914, https://openalex.org/W3122686941, https://openalex.org/W2393877243, https://openalex.org/W2090382595, https://openalex.org/W2046181616, https://openalex.org/W3139122440, https://openalex.org/W920448194, https://openalex.org/W1564806038 |
| cited_by_count | 3 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 2 |
| counts_by_year[1].year | 2022 |
| counts_by_year[1].cited_by_count | 1 |
| locations_count | 1 |
| best_oa_location.id | pmh:oai:arXiv.org:2009.03151 |
| 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/2009.03151 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | text |
| 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/2009.03151 |
| primary_location.id | pmh:oai:arXiv.org:2009.03151 |
| 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/2009.03151 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | text |
| 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/2009.03151 |
| publication_date | 2020-09-07 |
| publication_year | 2020 |
| referenced_works_count | 0 |
| abstract_inverted_index.R | 125 |
| abstract_inverted_index.a | 3, 38, 70, 102 |
| abstract_inverted_index.An | 124 |
| abstract_inverted_index.In | 52 |
| abstract_inverted_index.We | 77, 92 |
| abstract_inverted_index.an | 119 |
| abstract_inverted_index.as | 118 |
| abstract_inverted_index.be | 45 |
| abstract_inverted_index.is | 19, 131 |
| abstract_inverted_index.of | 41, 59, 108, 121 |
| abstract_inverted_index.on | 105, 113, 133 |
| abstract_inverted_index.to | 21, 44, 47, 83 |
| abstract_inverted_index.Act | 112 |
| abstract_inverted_index.Our | 16 |
| abstract_inverted_index.The | 34 |
| abstract_inverted_index.and | 23, 66 |
| abstract_inverted_index.but | 26 |
| abstract_inverted_index.for | 9, 61, 74, 85, 87, 127 |
| abstract_inverted_index.new | 17 |
| abstract_inverted_index.not | 28 |
| abstract_inverted_index.our | 122 |
| abstract_inverted_index.set | 40 |
| abstract_inverted_index.the | 49, 53, 57, 63, 75, 88, 94, 106, 114 |
| abstract_inverted_index.Fair | 109 |
| abstract_inverted_index.This | 0 |
| abstract_inverted_index.Wage | 111 |
| abstract_inverted_index.also | 78 |
| abstract_inverted_index.bias | 80 |
| abstract_inverted_index.both | 62 |
| abstract_inverted_index.does | 27 |
| abstract_inverted_index.for, | 25 |
| abstract_inverted_index.many | 30 |
| abstract_inverted_index.more | 31 |
| abstract_inverted_index.rate | 116 |
| abstract_inverted_index.real | 103 |
| abstract_inverted_index.used | 46 |
| abstract_inverted_index.with | 13, 97 |
| abstract_inverted_index.allow | 84 |
| abstract_inverted_index.data. | 15 |
| abstract_inverted_index.first | 35 |
| abstract_inverted_index.paper | 1 |
| abstract_inverted_index.rates | 58 |
| abstract_inverted_index.stage | 36 |
| abstract_inverted_index.under | 69 |
| abstract_inverted_index.allows | 24, 37 |
| abstract_inverted_index.doubly | 4 |
| abstract_inverted_index.effect | 107 |
| abstract_inverted_index.linear | 72 |
| abstract_inverted_index.method | 130 |
| abstract_inverted_index.robust | 5, 20 |
| abstract_inverted_index.score. | 51 |
| abstract_inverted_index.second | 54 |
| abstract_inverted_index.stage, | 55 |
| abstract_inverted_index.Minimum | 110 |
| abstract_inverted_index.general | 39 |
| abstract_inverted_index.machine | 42 |
| abstract_inverted_index.method. | 123 |
| abstract_inverted_index.package | 126 |
| abstract_inverted_index.provide | 79 |
| abstract_inverted_index.effects. | 91 |
| abstract_inverted_index.estimate | 48 |
| abstract_inverted_index.evaluate | 93 |
| abstract_inverted_index.function | 68 |
| abstract_inverted_index.proposes | 2 |
| abstract_inverted_index.require, | 29 |
| abstract_inverted_index.studies. | 100 |
| abstract_inverted_index.Github.\n | 134 |
| abstract_inverted_index.available | 132 |
| abstract_inverted_index.estimator | 8, 18 |
| abstract_inverted_index.extensive | 98 |
| abstract_inverted_index.parameter | 65 |
| abstract_inverted_index.partially | 71 |
| abstract_inverted_index.treatment | 90 |
| abstract_inverted_index.two-stage | 6 |
| abstract_inverted_index.correction | 81 |
| abstract_inverted_index.estimating | 10 |
| abstract_inverted_index.parametric | 64 |
| abstract_inverted_index.procedures | 82 |
| abstract_inverted_index.propensity | 50 |
| abstract_inverted_index.simulation | 99 |
| abstract_inverted_index.we\nderive | 56 |
| abstract_inverted_index.convergence | 60 |
| abstract_inverted_index.performance | 96 |
| abstract_inverted_index.illustration | 120 |
| abstract_inverted_index.implementing | 128 |
| abstract_inverted_index.the\nunknown | 67 |
| abstract_inverted_index.unemployment | 115 |
| abstract_inverted_index.Additionally, | 101 |
| abstract_inverted_index.heterogeneous | 11, 89 |
| abstract_inverted_index.is\nperformed | 117 |
| abstract_inverted_index.observations. | 33 |
| abstract_inverted_index.specification | 73 |
| abstract_inverted_index.the\nproposed | 129 |
| abstract_inverted_index.data\nanalysis | 104 |
| abstract_inverted_index.finite\nsample | 95 |
| abstract_inverted_index.high-dimensional | 14 |
| abstract_inverted_index.regressors\nthan | 32 |
| abstract_inverted_index.valid\ninference | 86 |
| abstract_inverted_index.learning\nmethods | 43 |
| abstract_inverted_index.outcome\nequation. | 76 |
| abstract_inverted_index.treatment\neffects | 12 |
| abstract_inverted_index.model\nmiss-specifications | 22 |
| abstract_inverted_index.semiparametric\ndifference-in-difference | 7 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/8 |
| sustainable_development_goals[0].score | 0.7400000095367432 |
| sustainable_development_goals[0].display_name | Decent work and economic growth |
| citation_normalized_percentile.value | 0.60851298 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |