{"id":"https://openalex.org/W4383820428","doi":"https://doi.org/10.1201/9780429324222-8","title":"Causal inference on quantiles using propensity scores","display_name":"Causal inference on quantiles using propensity scores","publication_year":2023,"publication_date":"2023-07-10","ids":{"openalex":"https://openalex.org/W4383820428","doi":"https://doi.org/10.1201/9780429324222-8"},"language":"en","primary_location":{"id":"doi:10.1201/9780429324222-8","doi":"https://doi.org/10.1201/9780429324222-8","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1201/9780429324222-8","pdf_url":null,"source":{"id":"https://openalex.org/S4306467597","display_name":"Chapman & Hall/CRC eBooks","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/P4310320449","host_organization_name":"Informa","host_organization_lineage":["https://openalex.org/P4310320449"],"host_organization_lineage_names":["Informa"],"type":"ebook platform"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Bayesian Nonparametrics for Causal Inference and Missing Data","raw_type":"book-chapter"},"volume":{"id":"https://openalex.org/W4383820075","display_name":"Bayesian Nonparametrics for Causal Inference and Missing Data"},"type":"book-chapter","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5031529176","display_name":"Michael J. Daniels","orcid":"https://orcid.org/0000-0002-9856-9486","observed_orcids":["https://orcid.org/0000-0002-9856-9486"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Michael J. Daniels","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102957047","display_name":"Antonio R. Linero","orcid":"https://orcid.org/0000-0002-9531-5667","observed_orcids":["https://orcid.org/0000-0002-9531-5667","https://orcid.org/0009-0007-1464-8907"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Antonio Linero","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5102729009","display_name":"Jason A. Roy","orcid":"https://orcid.org/0000-0003-4237-0504","observed_orcids":["https://orcid.org/0000-0003-4237-0504"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jason Roy","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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":0.31835425,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"154","last_page":"164"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10845","display_name":"Advanced Causal Inference Techniques","score":0.9990000128746033,"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/T10845","display_name":"Advanced Causal Inference Techniques","score":0.9990000128746033,"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/T10136","display_name":"Statistical Methods and Inference","score":9.999999747378752e-05,"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/T13702","display_name":"Machine Learning in Healthcare","score":9.999999747378752e-05,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/propensity-score","display_name":"propensity score","score":0.8640000224113464},{"id":"https://openalex.org/keywords/bart","display_name":"BART","score":0.6430000066757202},{"id":"https://openalex.org/keywords/dirichlet-process-mixture-model","display_name":"Dirichlet process mixture model","score":0.8949999809265137},{"id":"https://openalex.org/keywords/outcome-regression","display_name":"outcome regression","score":0.6420000195503235},{"id":"https://openalex.org/keywords/uncertainty-quantification","display_name":"uncertainty quantification","score":0.671999990940094},{"id":"https://openalex.org/keywords/electronic-health-records","display_name":"electronic health records","score":0.7710000276565552},{"id":"https://openalex.org/keywords/acute-kidney-injury-prevention","display_name":"acute kidney injury prevention","score":0.8410000205039978},{"id":"https://openalex.org/keywords/confounding","display_name":"confounding","score":0.4830000102519989}],"concepts":[{"id":"https://openalex.org/C118671147","wikidata":"https://www.wikidata.org/wiki/Q578714","display_name":"Quantile","level":2,"score":0.8347035646438599},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.7042467594146729},{"id":"https://openalex.org/C17923572","wikidata":"https://www.wikidata.org/wiki/Q7250160","display_name":"Propensity score matching","level":2,"score":0.6787989139556885},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.599143922328949},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.5626883506774902},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.46520739793777466},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3326484262943268},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.31148821115493774},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.22489070892333984}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1201/9780429324222-8","doi":"https://doi.org/10.1201/9780429324222-8","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1201/9780429324222-8","pdf_url":null,"source":{"id":"https://openalex.org/S4306467597","display_name":"Chapman & Hall/CRC eBooks","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/P4310320449","host_organization_name":"Informa","host_organization_lineage":["https://openalex.org/P4310320449"],"host_organization_lineage_names":["Informa"],"type":"ebook platform"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Bayesian Nonparametrics for Causal Inference and Missing Data","raw_type":"book-chapter"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6240000128746033,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"},{"score":0.4000000059604645,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2793196007","https://openalex.org/W3174091463","https://openalex.org/W4308242964","https://openalex.org/W3172480810","https://openalex.org/W3126812739","https://openalex.org/W2951892940","https://openalex.org/W3082894717","https://openalex.org/W2084832142","https://openalex.org/W7027096149","https://openalex.org/W4403920460"],"abstract_inverted_index":{"We":[0,77],"show":[1],"how":[2],"to":[3,9,20,34,57,64,86],"implement":[4],"a":[5,59,65],"flexible":[6],"BART-DPM":[7],"approach":[8],"compute":[10],"estimates":[11],"and":[12],"quantify":[13],"uncertainty":[14],"in":[15],"quantile":[16],"causal":[17],"effects.":[18],"Due":[19],"the":[21,26,35,51,73],"outcome":[22],"regression":[23],"including":[24],"only":[25,55],"propensity":[27,52],"score":[28],"(a":[29],"single":[30],"covariate":[31],"as":[32],"opposed":[33,63],"entire":[36],"vector":[37],"of":[38,75],"confounders),":[39],"inference":[40],"should":[41],"be":[42],"more":[43],"robust":[44],"for":[45,90],"\u2018extreme\u2019":[46],"quantiles":[47],"since,":[48],"conditional":[49],"on":[50],"score,":[53],"we":[54],"need":[56],"estimate":[58],"two-dimensional":[60],"distribution":[61],"(as":[62],"p":[66,71],"+":[67],"1-dimensional":[68],"distribution,":[69],"where":[70],"is":[72],"number":[74],"confounders).":[76],"illustrate":[78],"this":[79],"using":[80],"electronic":[81],"health":[82],"records":[83],"(EHR)":[84],"data":[85],"assess":[87],"two":[88],"therapies":[89],"preventing":[91],"acute":[92],"kidney":[93],"infection":[94],"(AKI).":[95]},"counts_by_year":[],"updated_date":"2026-10-14T16:39:01.820549","created_date":"2023-07-11T00:00:00"}