{"id":"https://openalex.org/W7166834823","doi":"https://doi.org/10.18653/v1/2026.acl-long.886","title":"Causal-ESC: Reliable Policy Learning for Emotional Support Conversation via Causal Inference","display_name":"Causal-ESC: Reliable Policy Learning for Emotional Support Conversation via Causal Inference","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7166834823","doi":"https://doi.org/10.18653/v1/2026.acl-long.886"},"language":null,"primary_location":{"id":"doi:10.18653/v1/2026.acl-long.886","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.acl-long.886","pdf_url":"https://aclanthology.org/2026.acl-long.886.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2026.acl-long.886.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139740998","display_name":"Xv Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xv Wang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139781183","display_name":"Wang Zhenyu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang Zhenyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139754081","display_name":"Guanyu Zheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guanyu Zheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139848855","display_name":"Rui Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rui Zhang","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":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.8245824,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"19386","last_page":"19401"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.26249998807907104,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.26249998807907104,"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":0.2231999933719635,"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":0.0649000033736229,"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/conversation","display_name":"Conversation","score":0.5958999991416931},{"id":"https://openalex.org/keywords/causal-inference","display_name":"Causal inference","score":0.4627000093460083},{"id":"https://openalex.org/keywords/causal-model","display_name":"Causal model","score":0.43149998784065247},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.3727000057697296},{"id":"https://openalex.org/keywords/counterfactual-thinking","display_name":"Counterfactual thinking","score":0.33309999108314514},{"id":"https://openalex.org/keywords/cognitive-reframing","display_name":"Cognitive reframing","score":0.3303000032901764}],"concepts":[{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.6164000034332275},{"id":"https://openalex.org/C2777200299","wikidata":"https://www.wikidata.org/wiki/Q52943","display_name":"Conversation","level":2,"score":0.5958999991416931},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.5464000105857849},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.4627000093460083},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.43149998784065247},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3946000039577484},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.3752000033855438},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.3727000057697296},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34389999508857727},{"id":"https://openalex.org/C108650721","wikidata":"https://www.wikidata.org/wiki/Q1783253","display_name":"Counterfactual thinking","level":2,"score":0.33309999108314514},{"id":"https://openalex.org/C187029079","wikidata":"https://www.wikidata.org/wiki/Q958679","display_name":"Cognitive reframing","level":2,"score":0.3303000032901764},{"id":"https://openalex.org/C2779436431","wikidata":"https://www.wikidata.org/wiki/Q30672407","display_name":"Policy learning","level":2,"score":0.3133000135421753},{"id":"https://openalex.org/C64357122","wikidata":"https://www.wikidata.org/wiki/Q1149766","display_name":"Causality (physics)","level":2,"score":0.30970001220703125},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.29089999198913574},{"id":"https://openalex.org/C2780829048","wikidata":"https://www.wikidata.org/wiki/Q1624720","display_name":"Conversation analysis","level":3,"score":0.28949999809265137},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.27489998936653137},{"id":"https://openalex.org/C44725695","wikidata":"https://www.wikidata.org/wiki/Q288156","display_name":"Normative","level":2,"score":0.25589999556541443}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2026.acl-long.886","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.acl-long.886","pdf_url":"https://aclanthology.org/2026.acl-long.886.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2026.acl-long.886","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.acl-long.886","pdf_url":"https://aclanthology.org/2026.acl-long.886.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7166834823.pdf","grobid_xml":"https://content.openalex.org/works/W7166834823.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"While":[0],"Large":[1],"Language":[2],"Models":[3],"(LLMs)":[4],"have":[5],"significantly":[6,155],"advanced":[7],"the":[8,45,98,109,172],"fluency":[9],"of":[10,38,101],"Emotional":[11],"Support":[12],"Conversation":[13],"(ESC)":[14],"systems,":[15],"current":[16],"research":[17],"predominantly":[18],"focuses":[19],"on":[20,104],"engineering":[21],"increasingly":[22],"complex":[23],"architectures-from":[24],"intricate":[25],"reasoning":[26],"chains":[27],"to":[28,57,62,95,125,171],"multi-agent":[29],"collaborations.While":[30],"these":[31,69,127],"advancements":[32],"(e.g.,":[33,141],"CoT)":[34],"offer":[35],"semantic":[36],"traces":[37],"reasoning,":[39],"they":[40],"remain":[41],"mechanistically":[42],"opaque,":[43],"obscuring":[44],"fundamental":[46],"causal":[47,99,130],"mechanisms":[48],"between":[49],"dialogue":[50,84],"features":[51,103],"and":[52,60,111,146,161],"effective":[53],"empathic":[54],"strategies,":[55],"leading":[56],"poor":[58],"interpretability":[59,174],"susceptibility":[61],"distribution":[63],"shifts":[64],"in":[65,115,159,176],"offline":[66,116],"learning.To":[67],"address":[68],"limitations,":[70],"we":[71],"propose":[72],"a":[73,166],"novel":[74],"framework":[75,152],"Causal-ESC.Departing":[76],"from":[77],"conventional":[78],"paradigms":[79],"that":[80,150],"directly":[81],"utilize":[82],"raw":[83],"history":[85],"as":[86],"input,":[87],"our":[88,151],"approach":[89],"introduces":[90],"Doubly":[91],"Robust":[92],"(DR)":[93],"learning":[94],"explicitly":[96],"model":[97],"effect":[100],"utterance":[102],"strategy":[105],"selection,":[106],"effectively":[107],"mitigating":[108],"biases":[110],"counterfactual":[112],"unobservability":[113],"inherent":[114],"datasets.We":[117],"further":[118],"integrate":[119],"an":[120],"LLM-based":[121],"stylized":[122],"rewriting":[123],"mechanism":[124],"translate":[126],"rigorously":[128],"learned":[129],"strategies":[131],"into":[132],"natural,":[133],"context-consistent":[134],"responses.Comprehensive":[135],"experiments,":[136],"supported":[137],"by":[138],"statistical":[139],"verification":[140],"Outcome":[142],"R":[143],"2":[144],")":[145],"human-like":[147],"evaluation,":[148],"demonstrate":[149],"not":[153],"only":[154],"outperforms":[156],"state-of-the-art":[157],"baselines":[158],"empathy":[160],"helpfulness":[162],"but":[163],"also":[164],"provides":[165],"theoretically":[167],"grounded,":[168],"interpretable":[169],"solution":[170],"mechanistic":[173],"dilemma":[175],"affective":[177],"computing.":[178]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-07-02T00:00:00"}