{"id":"https://openalex.org/W7133925901","doi":"https://doi.org/10.48550/arxiv.2603.03785","title":"Observationally Informed Adaptive Causal Experimental Design","display_name":"Observationally Informed Adaptive Causal Experimental Design","publication_year":2026,"publication_date":"2026-03-04","ids":{"openalex":"https://openalex.org/W7133925901","doi":"https://doi.org/10.48550/arxiv.2603.03785"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2603.03785","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","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":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5116306956","display_name":"Erdun Gao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gao, Erdun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128138527","display_name":"Liang Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Liang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128215579","display_name":"Jake Fawkes","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fawkes, Jake","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128145537","display_name":"Aoqi Zuo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zuo, Aoqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128128565","display_name":"Wenqin Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Wenqin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128207559","display_name":"Haoxuan Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Haoxuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128198895","display_name":"Mingming Gong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gong, Mingming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5128205596","display_name":"Dino Sejdinovic","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sejdinovic, Dino","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":0.5372999906539917,"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":0.5372999906539917,"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":0.2946999967098236,"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":0.029899999499320984,"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/causal-inference","display_name":"Causal inference","score":0.6862999796867371},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.6531000137329102},{"id":"https://openalex.org/keywords/observational-study","display_name":"Observational study","score":0.5830000042915344},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.5468999743461609},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5267999768257141},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4690000116825104},{"id":"https://openalex.org/keywords/operationalization","display_name":"Operationalization","score":0.41100001335144043},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.38760000467300415}],"concepts":[{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.6862999796867371},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.6531000137329102},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6463000178337097},{"id":"https://openalex.org/C23131810","wikidata":"https://www.wikidata.org/wiki/Q818574","display_name":"Observational study","level":2,"score":0.5830000042915344},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.5468999743461609},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5382000207901001},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5267999768257141},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4690000116825104},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4603999853134155},{"id":"https://openalex.org/C9354725","wikidata":"https://www.wikidata.org/wiki/Q286017","display_name":"Operationalization","level":2,"score":0.41100001335144043},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.38760000467300415},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.3734000027179718},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3702000081539154},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.36079999804496765},{"id":"https://openalex.org/C40422974","wikidata":"https://www.wikidata.org/wiki/Q741248","display_name":"Assertion","level":2,"score":0.3467999994754791},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.34139999747276306},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.30970001220703125},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.30140000581741333},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.2973000109195709},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.2962999939918518},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.28529998660087585},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.27950000762939453},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.2759000062942505},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.26600000262260437},{"id":"https://openalex.org/C155108698","wikidata":"https://www.wikidata.org/wiki/Q1231081","display_name":"Randomized experiment","level":2,"score":0.26339998841285706}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2603.03785","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2603.03785","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.03785","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:doi:10.48550/arxiv.2603.03785","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"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":{"Randomized":[0],"Controlled":[1],"Trials":[2],"(RCTs)":[3],"represent":[4],"the":[5,65,75,88,100,136,168],"gold":[6],"standard":[7,139],"for":[8,27,174,180],"causal":[9,81,169],"inference":[10],"yet":[11],"remain":[12],"a":[13,60,69,110,162,197],"scarce":[14],"resource.":[15],"While":[16],"large-scale":[17],"observational":[18,66,93],"data":[19,46],"is":[20,24,49,200],"often":[21],"available,":[22],"it":[23],"utilized":[25],"only":[26],"retrospective":[28],"fusion,":[29],"and":[30,129,185],"remains":[31],"discarded":[32],"in":[33,138],"prospective":[34],"trial":[35],"design":[36],"due":[37],"to":[38,85,91],"bias":[39],"concerns.":[40],"We":[41,154],"argue":[42],"this":[43,53],"\"tabula":[44],"rasa\"":[45],"acquisition":[47,141],"strategy":[48],"fundamentally":[50],"inefficient.":[51],"In":[52],"work,":[54],"we":[55,98,104,134],"propose":[56,155],"Active":[57],"Residual":[58],"Learning,":[59],"new":[61],"paradigm":[62],"that":[63,115,145,165,189,195],"leverages":[64],"model":[67,199],"as":[68],"foundational":[70],"prior.":[71],"This":[72],"approach":[73],"shifts":[74],"experimental":[76],"focus":[77],"from":[78,83,207],"learning":[79,205],"target":[80],"quantities":[82],"scratch":[84],"efficiently":[86],"estimating":[87,116],"residuals":[89],"required":[90],"correct":[92],"bias.":[94],"To":[95],"operationalize":[96],"this,":[97],"introduce":[99],"R-Design":[101,190],"framework.":[102],"Theoretically,":[103],"establish":[105],"two":[106],"key":[107],"advantages:":[108],"(1)":[109],"structural":[111],"efficiency":[112],"gap,":[113],"proving":[114],"smooth":[117],"residual":[118,172],"contrasts":[119],"admits":[120],"strictly":[121],"faster":[122],"convergence":[123],"rates":[124],"than":[125,204],"reconstructing":[126],"full":[127],"outcomes;":[128],"(2)":[130],"information":[131],"efficiency,":[132],"where":[133],"quantify":[135],"redundancy":[137],"parameter-based":[140],"(e.g.,":[142],"BALD),":[143],"demonstrating":[144],"such":[146],"baselines":[147],"waste":[148],"budget":[149],"on":[150,183],"task-irrelevant":[151],"nuisance":[152],"uncertainty.":[153],"R-EPIG":[156],"(Residual":[157],"Expected":[158],"Predictive":[159],"Information":[160],"Gain),":[161],"unified":[163],"criterion":[164],"directly":[166],"targets":[167],"estimand,":[170],"minimizing":[171],"uncertainty":[173],"estimation":[175],"or":[176],"clarifying":[177],"decision":[178],"boundaries":[179],"policy.":[181],"Experiments":[182],"synthetic":[184],"semi-synthetic":[186],"benchmarks":[187],"demonstrate":[188],"significantly":[191],"outperforms":[192],"baselines,":[193],"confirming":[194],"repairing":[196],"biased":[198],"far":[201],"more":[202],"efficient":[203],"one":[206],"scratch.":[208]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-06T00:00:00"}