{"id":"https://openalex.org/W7160949460","doi":"https://doi.org/10.48550/arxiv.2605.10590","title":"Amortizing Causal Sensitivity Analysis via Prior Data-Fitted Networks","display_name":"Amortizing Causal Sensitivity Analysis via Prior Data-Fitted Networks","publication_year":2026,"publication_date":"2026-05-11","ids":{"openalex":"https://openalex.org/W7160949460","doi":"https://doi.org/10.48550/arxiv.2605.10590"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.10590","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.10590","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.10590","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5120058806","display_name":"Emil Javurek","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Javurek, Emil","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135922102","display_name":"Dennis Frauen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Frauen, Dennis","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135977113","display_name":"Marie Brockschmidt","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Brockschmidt, Marie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072558230","display_name":"Jonas Schweisthal","orcid":"https://orcid.org/0000-0003-3725-3821"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Schweisthal, Jonas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135970462","display_name":"Stefan Feuerriegel","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Feuerriegel, Stefan","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/T10845","display_name":"Advanced Causal Inference Techniques","score":0.6434000134468079,"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":0.6434000134468079,"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":0.28209999203681946,"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/T12303","display_name":"Tensor decomposition and applications","score":0.008899999782443047,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"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/sensitivity","display_name":"Sensitivity (control systems)","score":0.8219000101089478},{"id":"https://openalex.org/keywords/causal-model","display_name":"Causal model","score":0.453000009059906},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4090999960899353},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.39629998803138733},{"id":"https://openalex.org/keywords/causal-inference","display_name":"Causal inference","score":0.38040000200271606},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.3743000030517578},{"id":"https://openalex.org/keywords/amortizing-loan","display_name":"Amortizing loan","score":0.35269999504089355}],"concepts":[{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.8219000101089478},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5234000086784363},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.453000009059906},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.42170000076293945},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4090999960899353},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.39629998803138733},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.38040000200271606},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3743000030517578},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.35519999265670776},{"id":"https://openalex.org/C51034333","wikidata":"https://www.wikidata.org/wiki/Q4747796","display_name":"Amortizing loan","level":5,"score":0.35269999504089355},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3418999910354614},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.3310000002384186},{"id":"https://openalex.org/C64357122","wikidata":"https://www.wikidata.org/wiki/Q1149766","display_name":"Causality (physics)","level":2,"score":0.32249999046325684},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.31949999928474426},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.3192000091075897},{"id":"https://openalex.org/C137635306","wikidata":"https://www.wikidata.org/wiki/Q182667","display_name":"Pareto principle","level":2,"score":0.2768000066280365},{"id":"https://openalex.org/C163504300","wikidata":"https://www.wikidata.org/wiki/Q2364925","display_name":"Causal structure","level":2,"score":0.2759000062942505},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2752000093460083},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.26190000772476196}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.10590","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.10590","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"doi:10.48550/arxiv.2605.10590","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.10590","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"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":{"Causal":[0],"sensitivity":[1,22,35,58,71,99,125,164,199],"analysis":[2,23,59],"aims":[3],"to":[4,30,56,110],"provide":[5],"bounds":[6,72,116],"for":[7,20,114,194,197],"causal":[8,21,33,57,121,161,198],"effect":[9,122],"estimates":[10],"in":[11],"the":[12,31,70,94,108,115,146,183,190],"presence":[13],"of":[14,96,107,150,176,185],"unobserved":[15],"confounding.":[16],"However,":[17],"existing":[18],"methods":[19],"are":[24,73],"per-instance":[25,180],"procedures,":[26],"meaning":[27],"that":[28,69,90,173],"changes":[29],"dataset,":[32],"query,":[34],"level,":[36],"or":[37],"treatment":[38,98],"require":[39],"new":[40],"computation.":[41],"Here,":[42],"we":[43,51,84,153],"instead":[44],"present":[45],"an":[46,53],"in-context":[47,195],"learning":[48,196],"approach.":[49],"Specifically,":[50],"propose":[52],"amortized":[54,156],"approach":[55,157,168],"based":[60],"on":[61],"prior-data":[62,88],"fitted":[63],"networks.":[64],"A":[65],"key":[66],"challenge":[67],"is":[68,91,174,189],"not":[74],"directly":[75],"available":[76],"when":[77],"sampling":[78],"training":[79,112],"data.":[80],"To":[81,182],"address":[82],"this,":[83],"develop":[85],"a":[86,104,118,170],"general":[87],"construction":[89,102],"applicable":[92],"across":[93,158],"class":[95],"generalized":[97],"models.":[100],"Our":[101],"involves":[103],"Lagrangian":[105],"scalarization":[106],"objective":[109,144],"generate":[111],"labels":[113],"through":[117],"tradeoff":[119],"between":[120],"min/max-imization":[123],"and":[124,140,163],"model":[126,193],"violation,":[127],"which":[128],"avoids":[129],"model-specific":[130],"analytical":[131],"derivations.":[132],"We":[133],"further":[134],"show":[135],"that,":[136],"under":[137],"standard":[138],"convexity":[139],"linearity":[141],"conditions,":[142],"our":[143,155,167,186],"recovers":[145],"full":[147],"Pareto":[148],"frontier":[149],"solutions.":[151],"Empirically,":[152],"demonstrate":[154],"various":[159],"datasets,":[160],"queries,":[162],"levels,":[165],"where":[166],"achieves":[169],"test-time":[171],"computation":[172],"orders":[175],"magnitude":[177],"faster":[178],"than":[179],"methods.":[181],"best":[184],"knowledge,":[187],"ours":[188],"first":[191],"foundation":[192],"analysis.":[200]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-13T00:00:00"}
