{"id":"https://openalex.org/W7160638813","doi":"https://doi.org/10.48550/arxiv.2605.06385","title":"Data-Driven Covariate Selection for Nonparametric and Cycle-Agnostic Causal Effect Estimation","display_name":"Data-Driven Covariate Selection for Nonparametric and Cycle-Agnostic Causal Effect Estimation","publication_year":2026,"publication_date":"2026-05-07","ids":{"openalex":"https://openalex.org/W7160638813","doi":"https://doi.org/10.48550/arxiv.2605.06385"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.06385","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.06385","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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.06385","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5113296063","display_name":"Ana Let\u00edcia Garcez Vicente","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vicente, Ana Leticia Garcez","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5119007014","display_name":"Gijs van Seeventer","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"van Seeventer, Gijs","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5072294850","display_name":"Saber Salehkaleybar","orcid":"https://orcid.org/0000-0003-3934-9931"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Salehkaleybar, Saber","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":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.8535000085830688,"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"}},"topics":[{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.8535000085830688,"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/T10845","display_name":"Advanced Causal Inference Techniques","score":0.12080000340938568,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.002099999925121665,"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/covariate","display_name":"Covariate","score":0.8543999791145325},{"id":"https://openalex.org/keywords/conditional-independence","display_name":"Conditional independence","score":0.697700023651123},{"id":"https://openalex.org/keywords/nonparametric-statistics","display_name":"Nonparametric statistics","score":0.5532000064849854},{"id":"https://openalex.org/keywords/independence","display_name":"Independence (probability theory)","score":0.5098000168800354},{"id":"https://openalex.org/keywords/causal-model","display_name":"Causal model","score":0.4975000023841858},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.48350000381469727},{"id":"https://openalex.org/keywords/confounding","display_name":"Confounding","score":0.41359999775886536},{"id":"https://openalex.org/keywords/observational-study","display_name":"Observational study","score":0.40310001373291016},{"id":"https://openalex.org/keywords/causality","display_name":"Causality (physics)","score":0.39329999685287476}],"concepts":[{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.8543999791145325},{"id":"https://openalex.org/C79772020","wikidata":"https://www.wikidata.org/wiki/Q5159264","display_name":"Conditional independence","level":2,"score":0.697700023651123},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.5532000064849854},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.5440999865531921},{"id":"https://openalex.org/C35651441","wikidata":"https://www.wikidata.org/wiki/Q625303","display_name":"Independence (probability theory)","level":2,"score":0.5098000168800354},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.4975000023841858},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.48350000381469727},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.47450000047683716},{"id":"https://openalex.org/C77350462","wikidata":"https://www.wikidata.org/wiki/Q1125472","display_name":"Confounding","level":2,"score":0.41359999775886536},{"id":"https://openalex.org/C23131810","wikidata":"https://www.wikidata.org/wiki/Q818574","display_name":"Observational study","level":2,"score":0.40310001373291016},{"id":"https://openalex.org/C64357122","wikidata":"https://www.wikidata.org/wiki/Q1149766","display_name":"Causality (physics)","level":2,"score":0.39329999685287476},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.38350000977516174},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3813000023365021},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.36250001192092896},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3619999885559082},{"id":"https://openalex.org/C74197172","wikidata":"https://www.wikidata.org/wiki/Q1195339","display_name":"Directed acyclic graph","level":2,"score":0.35670000314712524},{"id":"https://openalex.org/C186215838","wikidata":"https://www.wikidata.org/wiki/Q772232","display_name":"Conditional expectation","level":2,"score":0.34389999508857727},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.3314000070095062},{"id":"https://openalex.org/C163504300","wikidata":"https://www.wikidata.org/wiki/Q2364925","display_name":"Causal structure","level":2,"score":0.3077000081539154},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.29670000076293945},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.2924000024795532},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.28369998931884766},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.2797999978065491},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.27730000019073486},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.26429998874664307},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.2632000148296356},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.2619999945163727},{"id":"https://openalex.org/C44492722","wikidata":"https://www.wikidata.org/wiki/Q327069","display_name":"Conditional probability","level":2,"score":0.2558000087738037}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.06385","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.06385","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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.06385","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.06385","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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Estimating":[0],"causal":[1,36,73,97,124,152],"effects":[2],"from":[3],"observational":[4],"data":[5],"requires":[6],"identifying":[7],"valid":[8],"adjustment":[9],"sets.":[10],"This":[11],"task":[12],"is":[13,64,88],"especially":[14],"challenging":[15],"in":[16,71,77,150],"realistic":[17],"settings":[18,136],"where":[19],"latent":[20],"confounding":[21],"and":[22,41,69,123,134],"feedback":[23],"loops":[24],"are":[25],"present.":[26],"Existing":[27],"approaches":[28],"typically":[29],"assume":[30],"acyclicity":[31],"or":[32],"rely":[33],"on":[34,57,104,120,143],"global":[35],"structure":[37],"learning,":[38],"limiting":[39],"applicability":[40],"computational":[42],"efficiency.":[43],"In":[44,99],"this":[45,62,142],"work,":[46],"we":[47,140],"study":[48],"a":[49,116],"local,":[50],"data-driven":[51],"method":[52,63,130],"for":[53],"covariate":[54,121],"selection":[55,122],"based":[56],"conditional":[58,108],"independence":[59,109],"information.":[60],"While":[61],"known":[65],"to":[66,89,95],"be":[67],"sound":[68],"complete":[70],"acyclic":[72,135],"models,":[74],"its":[75],"validity":[76],"the":[78,105,129],"presence":[79],"of":[80,107],"cycles":[81],"has":[82],"remained":[83],"unclear.":[84],"Our":[85],"main":[86],"contribution":[87],"show":[90],"that":[91,128],"these":[92],"guarantees":[93],"extend":[94],"cyclic":[96,133,151],"models.":[98,153],"particular,":[100],"our":[101],"result":[102],"relies":[103],"invariance":[106],"assertions":[110],"under":[111],"$\u03c3$-acyclification.":[112],"These":[113],"findings":[114],"establish":[115],"unified,":[117],"cycle-agnostic":[118],"perspective":[119],"effect":[125],"estimation,":[126],"showing":[127,147],"applies":[131],"across":[132],"without":[137],"modification.":[138],"Empirically,":[139],"validate":[141],"extensive":[144],"synthetic":[145],"data,":[146],"reliable":[148],"performance":[149]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-09T00:00:00"}