{"id":"https://openalex.org/W7148452588","doi":"https://doi.org/10.48550/arxiv.2604.00811","title":"Deconfounding Scores and Representation Learning for Causal Effect Estimation with Weak Overlap","display_name":"Deconfounding Scores and Representation Learning for Causal Effect Estimation with Weak Overlap","publication_year":2026,"publication_date":"2026-04-01","ids":{"openalex":"https://openalex.org/W7148452588","doi":"https://doi.org/10.48550/arxiv.2604.00811"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.00811","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.00811","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":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.00811","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5132792110","display_name":"Oscar Clivio","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Clivio, Oscar","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132786248","display_name":"Alexander D'Amour","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"D'Amour, Alexander","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011032570","display_name":"Alexander Franks","orcid":"https://orcid.org/0000-0002-9329-206X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Franks, Alexander","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000399563","display_name":"David Bruns-Smith","orcid":"https://orcid.org/0009-0000-9863-3882"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bruns-Smith, David","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132816235","display_name":"Chris Holmes","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Holmes, Chris","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5017768355","display_name":"Avi Feller","orcid":"https://orcid.org/0000-0001-7319-5468"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Feller, Avi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"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/T10845","display_name":"Advanced Causal Inference Techniques","score":0.9674999713897705,"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":0.9674999713897705,"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":0.024800000712275505,"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/T13051","display_name":"Qualitative Comparative Analysis Research","score":0.0015999999595806003,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.6730999946594238},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.5450000166893005},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5257999897003174},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4799000024795532},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4618000090122223},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.43860000371932983},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.42419999837875366},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.3815999925136566}],"concepts":[{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.6730999946594238},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.5450000166893005},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5257999897003174},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5115000009536743},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.49950000643730164},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4799000024795532},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4618000090122223},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.43860000371932983},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.42419999837875366},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.3815999925136566},{"id":"https://openalex.org/C207390915","wikidata":"https://www.wikidata.org/wiki/Q1230525","display_name":"Divergence (linguistics)","level":2,"score":0.38100001215934753},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36910000443458557},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.3668999969959259},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.33719998598098755},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32839998602867126},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.323199987411499},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.31310001015663147},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.30469998717308044},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.2632000148296356},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.260699987411499},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.25690001249313354},{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.2556000053882599},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.00811","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.00811","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.00811","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.00811","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":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Overlap,":[0],"also":[1],"known":[2],"as":[3,89],"positivity,":[4],"is":[5,32],"a":[6,51,84,95,105,111],"key":[7],"condition":[8],"for":[9,104],"causal":[10],"treatment":[11,29],"effect":[12],"estimation.":[13],"Many":[14],"popular":[15],"estimators":[16],"suffer":[17],"from":[18],"high":[19,36],"variance":[20],"and":[21,63,71,121],"become":[22],"brittle":[23],"when":[24],"features":[25,120],"differ":[26],"strongly":[27],"across":[28],"groups.":[30],"This":[31],"especially":[33],"challenging":[34],"in":[35],"dimensions:":[37],"the":[38,64,68,80],"curse":[39],"of":[40,53,66,82,107,114],"dimensionality":[41],"can":[42],"make":[43],"overlap":[44,88,92],"implausible.":[45],"To":[46],"address":[47],"this,":[48],"we":[49],"propose":[50],"class":[52,106],"feature":[54],"representations":[55],"called":[56],"deconfounding":[57,96,108],"scores,":[58],"which":[59],"preserve":[60],"both":[61],"identification":[62],"target":[65],"estimation;":[67],"classical":[69],"propensity":[70],"prognostic":[72,124],"scores":[73,109,125],"are":[74,126],"two":[75],"special":[76],"cases.":[77],"We":[78,99,131],"characterize":[79],"problem":[81],"finding":[83],"representation":[85],"with":[86,118],"better":[87],"minimizing":[90],"an":[91],"divergence":[93],"under":[94,110],"score":[97],"constraint.":[98],"then":[100],"derive":[101],"closed-form":[102],"expressions":[103],"broad":[112],"family":[113],"generalized":[115],"linear":[116],"models":[117],"Gaussian":[119],"show":[122],"that":[123],"overlap-optimal":[127],"within":[128],"this":[129,137],"class.":[130],"conduct":[132],"extensive":[133],"experiments":[134],"to":[135],"assess":[136],"behavior":[138],"empirically.":[139]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-04-03T00:00:00"}