{"id":"https://openalex.org/W4387848546","doi":"https://doi.org/10.1145/3583780.3615096","title":"Treatment Effect Estimation across Domains","display_name":"Treatment Effect Estimation across Domains","publication_year":2023,"publication_date":"2023-10-21","ids":{"openalex":"https://openalex.org/W4387848546","doi":"https://doi.org/10.1145/3583780.3615096"},"language":"en","primary_location":{"id":"doi:10.1145/3583780.3615096","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3583780.3615096","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5070695930","display_name":"Yixuan Sun","orcid":"https://orcid.org/0000-0002-7493-5752"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Yi-Xuan Sun","raw_affiliation_strings":["Ant Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Ant Group, Hangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100619354","display_name":"Yalin Zhang","orcid":"https://orcid.org/0000-0002-3244-1084"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ya-Lin Zhang","raw_affiliation_strings":["Ant Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Ant Group, Hangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018466593","display_name":"Wei Wang","orcid":"https://orcid.org/0000-0001-7289-4822"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Wang","raw_affiliation_strings":["Nanjing University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100430936","display_name":"Longfei Li","orcid":"https://orcid.org/0000-0002-9263-7011"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Longfei Li","raw_affiliation_strings":["Ant Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Ant Group, Hangzhou, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045140292","display_name":"Jun Zhou","orcid":"https://orcid.org/0000-0001-6033-6102"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jun Zhou","raw_affiliation_strings":["Ant Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Ant Group, Hangzhou, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5070695930"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.6954,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.73166207,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"2352","last_page":"2361"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10845","display_name":"Advanced Causal Inference Techniques","score":0.9983999729156494,"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":0.9983999729156494,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.991599977016449,"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/T10136","display_name":"Statistical Methods and Inference","score":0.9708999991416931,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.7806044816970825},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7554023265838623},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6919792890548706},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5937033891677856},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.585448145866394},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5583789348602295},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.537834882736206},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.5366674661636353},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.4866521954536438},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48076578974723816},{"id":"https://openalex.org/keywords/causal-inference","display_name":"Causal inference","score":0.46952763199806213},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.17908215522766113},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.17883148789405823},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09339874982833862}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.7806044816970825},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7554023265838623},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6919792890548706},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5937033891677856},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.585448145866394},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5583789348602295},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.537834882736206},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.5366674661636353},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.4866521954536438},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48076578974723816},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.46952763199806213},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.17908215522766113},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.17883148789405823},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09339874982833862},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3583780.3615096","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3583780.3615096","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1516659296","https://openalex.org/W1618905105","https://openalex.org/W2028138594","https://openalex.org/W2034368206","https://openalex.org/W2064903582","https://openalex.org/W2112483442","https://openalex.org/W2117308775","https://openalex.org/W2126292488","https://openalex.org/W2150291618","https://openalex.org/W2164943005","https://openalex.org/W2208550830","https://openalex.org/W2305754340","https://openalex.org/W2593768305","https://openalex.org/W2604738573","https://openalex.org/W2607662938","https://openalex.org/W2624816748","https://openalex.org/W2913668833","https://openalex.org/W2914584698","https://openalex.org/W2964261049","https://openalex.org/W2999905431","https://openalex.org/W3014596384","https://openalex.org/W3022547535","https://openalex.org/W3124256299","https://openalex.org/W3124999902","https://openalex.org/W3134774296"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W2745001401","https://openalex.org/W4321353415","https://openalex.org/W2130974462","https://openalex.org/W972276598","https://openalex.org/W4246352526","https://openalex.org/W2028665553","https://openalex.org/W4230315250","https://openalex.org/W2086519370","https://openalex.org/W2087343574"],"abstract_inverted_index":{"Treatment":[0],"effect":[1,42],"estimation":[2],"is":[3],"essential":[4],"in":[5,15,38,52,63,75,85,111,180],"the":[6,23,31,41,64,86,90,112,117,121,129,143,151,164,175,189,200,216],"causal":[7],"inference":[8],"literature,":[9],"which":[10,34,198,214],"has":[11],"attracted":[12],"increasing":[13],"attention":[14],"recent":[16],"years.":[17],"Most":[18],"previous":[19],"methods":[20],"assume":[21],"that":[22],"training":[24],"and":[25,80,123,131,146,211],"test":[26],"data":[27,114,210],"are":[28,101,206],"drawn":[29],"from":[30],"same":[32],"distribution,":[33],"may":[35,44,60,173],"not":[36,109],"hold":[37],"practice":[39],"since":[40],"estimators":[43,161],"need":[45],"to":[46,116,162],"be":[47,61,83],"deployed":[48],"across":[49],"domains.":[50],"Meanwhile,":[51],"real-world":[53],"applications,":[54],"little":[55],"or":[56],"no":[57],"targeted":[58],"treatments":[59,79],"conducted":[62,207],"new":[65],"domain.":[66],"Therefore,":[67],"we":[68,155,172,186],"focus":[69],"on":[70,208],"a":[71,136,195],"more":[72,202],"realistic":[73],"scenario":[74],"this":[76,104,181],"paper,":[77],"where":[78],"outcomes":[81],"can":[82],"observed":[84],"source":[87,113,130],"domain,":[88],"but":[89,126],"target":[91,132],"domain":[92],"only":[93,99,110],"contains":[94],"some":[95],"unlabeled":[96],"data,":[97],"i.e.,":[98],"features":[100],"available.":[102],"In":[103,150],"scenario,":[105],"thedistribution":[106],"shift":[107],"exists":[108],"due":[115],"selection":[118],"bias":[119],"between":[120,128],"control":[122],"treated":[124],"groups,":[125],"also":[127,187],"data.":[133],"We":[134],"propose":[135],"novel":[137],"direct":[138],"learning":[139],"framework":[140],"along":[141],"with":[142],"distribution":[144,152,168],"adaptation":[145,153],"reliable":[147,191],"scoring":[148,192],"modules.":[149],"module,":[154],"design":[156,188],"three":[157],"specialized":[158],"density":[159],"ratio":[160],"aid":[163],"issue":[165],"of":[166,177,218],"complex":[167],"shifts.":[169],"Even":[170],"so,":[171],"face":[174],"challenge":[176],"unreliable":[178],"pseudo-effects":[179],"framework.":[182],"To":[183],"address":[184],"that,":[185],"uncertainty-based":[190],"module":[193],"as":[194],"vital":[196],"support,":[197],"makes":[199],"method":[201],"reliable.":[203],"The":[204],"experiments":[205],"synthetic":[209],"benchmark":[212],"datasets,":[213],"demonstrate":[215],"superiority":[217],"our":[219],"method.":[220]},"counts_by_year":[{"year":2024,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}