{"id":"https://openalex.org/W3209242175","doi":"https://doi.org/10.1109/tit.2025.3618595","title":"Pearson Chi-Squared Conditional Randomization Test","display_name":"Pearson Chi-Squared Conditional Randomization Test","publication_year":2025,"publication_date":"2025-10-07","ids":{"openalex":"https://openalex.org/W3209242175","doi":"https://doi.org/10.1109/tit.2025.3618595","mag":"3209242175"},"language":"en","primary_location":{"id":"doi:10.1109/tit.2025.3618595","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tit.2025.3618595","pdf_url":null,"source":{"id":"https://openalex.org/S4502562","display_name":"IEEE Transactions on Information Theory","issn_l":"0018-9448","issn":["0018-9448","1557-9654"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Information Theory","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2111.00027","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5010342958","display_name":"Adel Javanmard","orcid":"https://orcid.org/0000-0003-1934-8747"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Adel Javanmard","raw_affiliation_strings":["Data Sciences and Operations Department, University of Southern California, Los Angeles, CA, USA"],"raw_orcid":"https://orcid.org/0000-0003-1934-8747","affiliations":[{"raw_affiliation_string":"Data Sciences and Operations Department, University of Southern California, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I1174212"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101620129","display_name":"Mohammad Mehrabi","orcid":"https://orcid.org/0000-0002-3503-533X"},"institutions":[{"id":"https://openalex.org/I4210137306","display_name":"Stanford Medicine","ror":"https://ror.org/03mtd9a03","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I4210137306","https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mohammad Mehrabi","raw_affiliation_strings":["Department of Operations, Information and Technology, Stanford Graduate School of Business, Stanford, CA, USA"],"raw_orcid":"https://orcid.org/0000-0002-3503-533X","affiliations":[{"raw_affiliation_string":"Department of Operations, Information and Technology, Stanford Graduate School of Business, Stanford, CA, USA","institution_ids":["https://openalex.org/I4210137306"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.00177242,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"71","issue":"12","first_page":"9617","last_page":"9646"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9915000200271606,"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/T10136","display_name":"Statistical Methods and Inference","score":0.9915000200271606,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9904000163078308,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11235","display_name":"Statistical Methods in Clinical Trials","score":0.9800000190734863,"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/conditional-independence","display_name":"Conditional independence","score":0.8127869367599487},{"id":"https://openalex.org/keywords/covariate","display_name":"Covariate","score":0.6966848969459534},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.6873117685317993},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.6636817455291748},{"id":"https://openalex.org/keywords/independence","display_name":"Independence (probability theory)","score":0.6253435611724854},{"id":"https://openalex.org/keywords/bonferroni-correction","display_name":"Bonferroni correction","score":0.5919907093048096},{"id":"https://openalex.org/keywords/conditional-probability-distribution","display_name":"Conditional probability distribution","score":0.45304396748542786},{"id":"https://openalex.org/keywords/statistical-hypothesis-testing","display_name":"Statistical hypothesis testing","score":0.4241794943809509},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.4230997562408447},{"id":"https://openalex.org/keywords/sample-size-determination","display_name":"Sample size determination","score":0.41652753949165344},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3374261260032654}],"concepts":[{"id":"https://openalex.org/C79772020","wikidata":"https://www.wikidata.org/wiki/Q5159264","display_name":"Conditional independence","level":2,"score":0.8127869367599487},{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.6966848969459534},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6873117685317993},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.6636817455291748},{"id":"https://openalex.org/C35651441","wikidata":"https://www.wikidata.org/wiki/Q625303","display_name":"Independence (probability theory)","level":2,"score":0.6253435611724854},{"id":"https://openalex.org/C127808970","wikidata":"https://www.wikidata.org/wiki/Q385989","display_name":"Bonferroni correction","level":2,"score":0.5919907093048096},{"id":"https://openalex.org/C43555835","wikidata":"https://www.wikidata.org/wiki/Q2300258","display_name":"Conditional probability distribution","level":2,"score":0.45304396748542786},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.4241794943809509},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4230997562408447},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.41652753949165344},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3374261260032654}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/tit.2025.3618595","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tit.2025.3618595","pdf_url":null,"source":{"id":"https://openalex.org/S4502562","display_name":"IEEE Transactions on Information Theory","issn_l":"0018-9448","issn":["0018-9448","1557-9654"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Information Theory","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2111.00027","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2111.00027","pdf_url":"https://arxiv.org/pdf/2111.00027","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"mag:3209242175","is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/2111.00027","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.2111.00027","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2111.00027","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":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2111.00027","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2111.00027","pdf_url":"https://arxiv.org/pdf/2111.00027","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.49000000953674316}],"awards":[{"id":"https://openalex.org/G4023349816","display_name":"Robust and scalable algorithms for learning hidden structures in sparse network data with the aid of side information","funder_award_id":"2311024","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6509522725","display_name":"CAREER: Valid and Scalable Inference for High-dimensional Statistical Models","funder_award_id":"1844481","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":55,"referenced_works":["https://openalex.org/W30853191","https://openalex.org/W1511986666","https://openalex.org/W1553101044","https://openalex.org/W1656304533","https://openalex.org/W1990534247","https://openalex.org/W1990621008","https://openalex.org/W2001088761","https://openalex.org/W2045638068","https://openalex.org/W2047923046","https://openalex.org/W2064815984","https://openalex.org/W2075448493","https://openalex.org/W2104648144","https://openalex.org/W2110065044","https://openalex.org/W2111992537","https://openalex.org/W2112814716","https://openalex.org/W2119471399","https://openalex.org/W2120846249","https://openalex.org/W2125631472","https://openalex.org/W2135046866","https://openalex.org/W2165700458","https://openalex.org/W2339195677","https://openalex.org/W2407174002","https://openalex.org/W2463154922","https://openalex.org/W2541669241","https://openalex.org/W2592411267","https://openalex.org/W2625479840","https://openalex.org/W2772190429","https://openalex.org/W2782893163","https://openalex.org/W2801890059","https://openalex.org/W2898721507","https://openalex.org/W2913149853","https://openalex.org/W2951039901","https://openalex.org/W2962717928","https://openalex.org/W2963068813","https://openalex.org/W2963371845","https://openalex.org/W2963535485","https://openalex.org/W2963642566","https://openalex.org/W2964060211","https://openalex.org/W2971169014","https://openalex.org/W2981677239","https://openalex.org/W2981798823","https://openalex.org/W2999595535","https://openalex.org/W3005202443","https://openalex.org/W3005788930","https://openalex.org/W3024144669","https://openalex.org/W3034059451","https://openalex.org/W3038472364","https://openalex.org/W3042860357","https://openalex.org/W3092028542","https://openalex.org/W3098615150","https://openalex.org/W3104291374","https://openalex.org/W3107006374","https://openalex.org/W3135927585","https://openalex.org/W3135956136","https://openalex.org/W3157881304"],"related_works":["https://openalex.org/W3046847860","https://openalex.org/W2884272234","https://openalex.org/W3042860357","https://openalex.org/W3168089616","https://openalex.org/W1911468658","https://openalex.org/W2739785167","https://openalex.org/W2150088195","https://openalex.org/W2950896838","https://openalex.org/W3185155209","https://openalex.org/W3092028542","https://openalex.org/W3004027363","https://openalex.org/W2852089015","https://openalex.org/W3087618178","https://openalex.org/W3032943956","https://openalex.org/W2017327696","https://openalex.org/W1967839783","https://openalex.org/W3035223589","https://openalex.org/W2071644575","https://openalex.org/W2007883165","https://openalex.org/W2014056762"],"abstract_inverted_index":{"Conditional":[0,61],"independence":[1,23],"(CI)":[2],"testing":[3],"arises":[4],"naturally":[5],"in":[6,138,176,187,198],"many":[7],"scientific":[8],"problems":[9],"and":[10,31,75,125],"applications":[11],"domains.":[12],"The":[13],"goal":[14],"of":[15,42,87,99,108,116,119,128,134,140,153,190],"this":[16,51],"problem":[17],"is":[18,195],"to":[19,78,90,170],"investigate":[20],"the":[21,40,67,84,88,109,114,120,122,126,129,132,154,177,188,191,200],"conditional":[22,80,100,201],"between":[24],"a":[25,43,55,95,105,141,173],"response":[26],"variable":[27,33,46],"<italic":[28,34,47,72,203],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[29,35,48,73,162,180,204],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">Y</i>":[30],"another":[32],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">X</i>,":[36],"while":[37],"controlling":[38],"for":[39],"effect":[41,115],"high-dimensional":[44],"confounding":[45],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">Z</i>.":[49],"In":[50,147],"paper,":[52],"we":[53,149],"introduce":[54],"novel":[56],"test,":[57,64,111,121,156],"called":[58,143],"\u2018Pearson":[59],"Chi-squared":[60],"Randomization\u2019":[62],"(PCR)":[63],"which":[65,112,166,184],"uses":[66,167],"distributional":[68],"information":[69],"on":[70,172],"covariates":[71],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">X,Z</i>":[74],"constructs":[76],"randomizations":[77],"test":[79,192],"independence.":[81],"PCR":[82,110,155],"leverages":[83],"i.i.d-ness":[85],"property":[86],"observations":[89],"obtain":[91],"high-resolution":[92],"p-values":[93],"with":[94,157],"very":[96],"small":[97],"number":[98],"randomizations.":[101],"We":[102],"also":[103],"provide":[104],"power":[106],"analysis":[107],"captures":[113],"various":[117],"parameters":[118],"sample":[123],"size":[124,189],"distance":[127],"alternative":[130],"from":[131],"set":[133],"null":[135],"distributions,":[136],"measured":[137],"terms":[139],"notion":[142],"\u2018conditional":[144],"relative":[145],"density\u2019.":[146],"addition,":[148],"propose":[150],"two":[151],"extensions":[152],"important":[158],"practical":[159],"implications:":[160],"(<italic":[161,179],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">i</i>)":[163],"parameter-free":[164],"PCR,":[165,183],"Bonferroni\u2019s":[168],"correction":[169],"decide":[171],"tuning":[174],"parameter":[175],"test;":[178],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">ii</i>)":[181],"robust":[182],"avoids":[185],"inflations":[186],"when":[193],"there":[194],"slight":[196],"error":[197],"estimating":[199],"law":[202],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">P<sub>X|Z</sub></i>.":[205]},"counts_by_year":[],"updated_date":"2026-07-20T07:56:41.581041","created_date":"2025-10-10T00:00:00"}