{"id":"https://openalex.org/W7166654570","doi":"https://doi.org/10.48550/arxiv.2606.29681","title":"Sample-Efficient Learning of Probabilistic Causes for Reachability in Markov Decision Processes with Probabilistic Guarantees","display_name":"Sample-Efficient Learning of Probabilistic Causes for Reachability in Markov Decision Processes with Probabilistic Guarantees","publication_year":2026,"publication_date":"2026-06-29","ids":{"openalex":"https://openalex.org/W7166654570","doi":"https://doi.org/10.48550/arxiv.2606.29681"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.29681","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.29681","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.2606.29681","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5023077795","display_name":"Ryohei Oura","orcid":"https://orcid.org/0000-0001-9864-4506"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Oura, Ryohei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136231440","display_name":"Georgios Fainekos","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fainekos, Georgios","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035243085","display_name":"Hideki Okamoto","orcid":"https://orcid.org/0009-0009-2533-9581"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Okamoto, Hideki","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139706991","display_name":"Bardh Hoxha","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hoxha, Bardh","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":0.420199990272522,"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":0.420199990272522,"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/T10142","display_name":"Formal Methods in Verification","score":0.21940000355243683,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.028300000354647636,"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/reachability","display_name":"Reachability","score":0.9171000123023987},{"id":"https://openalex.org/keywords/markov-decision-process","display_name":"Markov decision process","score":0.8194000124931335},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.8091999888420105},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5613999962806702},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.5217000246047974},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.5048999786376953},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.490200012922287},{"id":"https://openalex.org/keywords/conditional-probability","display_name":"Conditional probability","score":0.4219000041484833}],"concepts":[{"id":"https://openalex.org/C136643341","wikidata":"https://www.wikidata.org/wiki/Q1361526","display_name":"Reachability","level":2,"score":0.9171000123023987},{"id":"https://openalex.org/C106189395","wikidata":"https://www.wikidata.org/wiki/Q176789","display_name":"Markov decision process","level":3,"score":0.8194000124931335},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.8091999888420105},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.675000011920929},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5613999962806702},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.5217000246047974},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.5048999786376953},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.490200012922287},{"id":"https://openalex.org/C44492722","wikidata":"https://www.wikidata.org/wiki/Q327069","display_name":"Conditional probability","level":2,"score":0.4219000041484833},{"id":"https://openalex.org/C64357122","wikidata":"https://www.wikidata.org/wiki/Q1149766","display_name":"Causality (physics)","level":2,"score":0.40959998965263367},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3747999966144562},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.374099999666214},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35089999437332153},{"id":"https://openalex.org/C17098449","wikidata":"https://www.wikidata.org/wiki/Q176814","display_name":"Partially observable Markov decision process","level":4,"score":0.3384999930858612},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.3343999981880188},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32030001282691956},{"id":"https://openalex.org/C143017306","wikidata":"https://www.wikidata.org/wiki/Q3318133","display_name":"Probabilistic relevance model","level":4,"score":0.3158999979496002},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2915000021457672},{"id":"https://openalex.org/C110251889","wikidata":"https://www.wikidata.org/wiki/Q1569697","display_name":"Model checking","level":2,"score":0.2890999913215637},{"id":"https://openalex.org/C52063229","wikidata":"https://www.wikidata.org/wiki/Q7246845","display_name":"Probabilistic CTL","level":4,"score":0.2802000045776367},{"id":"https://openalex.org/C115988155","wikidata":"https://www.wikidata.org/wiki/Q3262192","display_name":"Decision problem","level":2,"score":0.27709999680519104},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.2721000015735626},{"id":"https://openalex.org/C54907487","wikidata":"https://www.wikidata.org/wiki/Q7915688","display_name":"Variable-order Markov model","level":4,"score":0.27090001106262207},{"id":"https://openalex.org/C28901747","wikidata":"https://www.wikidata.org/wiki/Q177571","display_name":"Decision theory","level":2,"score":0.2678999900817871},{"id":"https://openalex.org/C2984634286","wikidata":"https://www.wikidata.org/wiki/Q1331926","display_name":"Decision process","level":2,"score":0.26089999079704285},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.2583000063896179}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.29681","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.29681","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.2606.29681","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.29681","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":[{"display_name":"Peace, Justice and strong institutions","score":0.7305812835693359,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Probabilistic":[0],"model":[1],"checking":[2,108],"for":[3,48,94],"Markov":[4],"decision":[5],"processes":[6],"(MDPs)":[7],"provides":[8],"quantitative":[9],"guarantees,":[10],"but":[11],"often":[12],"offers":[13],"limited":[14],"insight":[15],"into":[16],"why":[17],"undesired":[18],"outcomes":[19],"occur.":[20],"Probability-raising":[21],"(PR)":[22],"causality":[23],"addresses":[24],"this":[25],"by":[26],"identifying":[27],"states":[28,142],"whose":[29],"visitation":[30],"increases":[31],"the":[32,50,72,119],"probability":[33],"of":[34,71,118,157],"reaching":[35],"designated":[36],"states.":[37],"Existing":[38],"PR-cause":[39,95,107],"identification":[40,156],"methods,":[41],"however,":[42],"use":[43],"MDP":[44,103],"modifications":[45],"not":[46],"well-suited":[47],"learning:":[49],"gap":[51],"between":[52],"conditional":[53,111],"and":[54,66,86,128,154],"unconditional":[55],"reachability":[56,69,112,116],"probabilities":[57,70,79],"can":[58],"be":[59],"hard":[60],"to":[61,109],"detect":[62],"from":[63],"transition":[64,78],"samples,":[65],"construction":[67],"requires":[68],"MDP,":[73],"which":[74],"are":[75,80],"unavailable":[76],"when":[77],"unknown.":[81],"We":[82,122],"study":[83],"unknown":[84],"MDPs":[85],"propose":[87],"a":[88,101],"learning":[89],"approach":[90],"with":[91],"probabilistic":[92],"guarantees":[93],"identification.":[96],"Our":[97],"key":[98],"ingredient":[99],"is":[100],"restart-based":[102],"modification":[104],"that":[105,139],"reduces":[106],"two":[110,150],"queries":[113],"without":[114],"using":[115],"values":[117],"original":[120],"MDP.":[121],"prove":[123],"correctness,":[124],"establish":[125],"sample-complexity":[126],"bounds,":[127],"develop":[129],"an":[130],"anytime":[131],"learning-and-checking":[132],"algorithm":[133],"based":[134],"on":[135,149],"two-sided":[136],"value":[137],"iteration":[138],"progressively":[140],"classifies":[141],"as":[143],"causal,":[144],"non-causal,":[145],"or":[146],"undecided.":[147],"Experiments":[148],"benchmarks":[151],"demonstrate":[152],"reliable":[153],"fast":[155],"PR":[158],"causes.":[159]},"counts_by_year":[],"updated_date":"2026-07-01T06:29:00.853634","created_date":"2026-07-01T00:00:00"}
