{"id":"https://openalex.org/W2911523443","doi":"https://doi.org/10.1145/3308558.3313744","title":"Multiple Treatment Effect Estimation using Deep Generative Model with Task Embedding","display_name":"Multiple Treatment Effect Estimation using Deep Generative Model with Task Embedding","publication_year":2019,"publication_date":"2019-05-13","ids":{"openalex":"https://openalex.org/W2911523443","doi":"https://doi.org/10.1145/3308558.3313744","mag":"2911523443"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1145/3308558.3313744","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-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/A5055018529","display_name":"Shiv Kumar Saini","orcid":"https://orcid.org/0000-0001-6568-7104"},"institutions":[],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Shiv Kumar Saini","raw_affiliation_strings":["Adobe Research, India"],"affiliations":[{"raw_affiliation_string":"Adobe Research, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086986253","display_name":"Sunny Dhamnani","orcid":null},"institutions":[],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Sunny Dhamnani","raw_affiliation_strings":["Adobe Research, India"],"affiliations":[{"raw_affiliation_string":"Adobe Research, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024151648","display_name":"Akil Arif Ibrahim","orcid":null},"institutions":[{"id":"https://openalex.org/I154851008","display_name":"Indian Institute of Technology Roorkee","ror":"https://ror.org/00582g326","country_code":"IN","type":"education","lineage":["https://openalex.org/I154851008"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Akil Arif Ibrahim","raw_affiliation_strings":["Indian Institute of Technology Roorkee, India"],"affiliations":[{"raw_affiliation_string":"Indian Institute of Technology Roorkee, India","institution_ids":["https://openalex.org/I154851008"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5024149577","display_name":"Prithviraj Chavan","orcid":null},"institutions":[],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Prithviraj Chavan","raw_affiliation_strings":["Adobe, India"],"affiliations":[{"raw_affiliation_string":"Adobe, India","institution_ids":[]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.651,"has_fulltext":true,"fulltext_origin":"ngrams","cited_by_count":16,"citation_normalized_percentile":{"value":0.950604,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":91},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T11161","display_name":"Economic Analysis of Retail and Marketing Strategies","score":0.9953,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11161","display_name":"Economic Analysis of Retail and Marketing Strategies","score":0.9953,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11235","display_name":"Statistical Methods in Clinical Trials and Drug Development","score":0.9938,"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/T10845","display_name":"Methods for Causal Inference in Observational Studies","score":0.993,"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/treatment-effects","display_name":"Treatment Effects","score":0.555059},{"id":"https://openalex.org/keywords/multiple-testing","display_name":"Multiple Testing","score":0.531893},{"id":"https://openalex.org/keywords/pharmacokinetic/pharmacodynamic-modeling","display_name":"Pharmacokinetic/Pharmacodynamic Modeling","score":0.524076},{"id":"https://openalex.org/keywords/causal-inference","display_name":"Causal Inference","score":0.514809},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.460373},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.4492702}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.65611684},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6461729},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.6164525},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5976888},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5625771},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5372429},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.48234576},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.47929993},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.47642216},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4625878},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.460373},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.4492702},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.4405071},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.25944746},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19176555},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.17942366},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C144237770","wikidata":"https://www.wikidata.org/wiki/Q747534","display_name":"Mathematical economics","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1145/3308558.3313744","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.54,"display_name":"Peace, justice, and strong institutions","id":"https://metadata.un.org/sdg/16"}],"grants":[],"datasets":[],"versions":[],"referenced_works_count":34,"referenced_works":["https://openalex.org/W1499798934","https://openalex.org/W1678356000","https://openalex.org/W1978209044","https://openalex.org/W1991898766","https://openalex.org/W1996016456","https://openalex.org/W2009551863","https://openalex.org/W2017679397","https://openalex.org/W2025348367","https://openalex.org/W2064903582","https://openalex.org/W2099471712","https://openalex.org/W2101234009","https://openalex.org/W2118953563","https://openalex.org/W2121756366","https://openalex.org/W2126292488","https://openalex.org/W2132917208","https://openalex.org/W2143891888","https://openalex.org/W2164762854","https://openalex.org/W2389937032","https://openalex.org/W2397969444","https://openalex.org/W2539792571","https://openalex.org/W2562820184","https://openalex.org/W2574729525","https://openalex.org/W2620393362","https://openalex.org/W2785777814","https://openalex.org/W2911964244","https://openalex.org/W2953384591","https://openalex.org/W2962695761","https://openalex.org/W3105753181","https://openalex.org/W3143949042","https://openalex.org/W3150893739","https://openalex.org/W4238503602","https://openalex.org/W4246418466","https://openalex.org/W4295097398","https://openalex.org/W941378780"],"related_works":["https://openalex.org/W4386114435","https://openalex.org/W4384300587","https://openalex.org/W4320518385","https://openalex.org/W3216348315","https://openalex.org/W3080837820","https://openalex.org/W2957103736","https://openalex.org/W2785968631","https://openalex.org/W2301199057","https://openalex.org/W2080556896","https://openalex.org/W2012618330"],"abstract_inverted_index":{"Causal":[0],"inference":[1,28,60,157],"using":[2,61,138],"observational":[3],"data":[4,235],"on":[5,26,109,133,233],"multiple":[6,42,48,86,103],"treatments":[7,49,87],"is":[8,50,107,127,147,179,216,228],"an":[9],"important":[10],"problem":[11],"in":[12,29,154,171],"a":[13,55,67,91,176,223],"wide":[14],"variety":[15],"of":[16,31,58,75,78,85,120,225,239],"fields.":[17],"However,":[18],"the":[19,72,79,98,116,125,139,142,155,169,172,187,194,203,226,237,240,246],"existing":[20],"literature":[21,158],"tends":[22],"to":[23,47,70,100,102,114,186,219],"focus":[24],"only":[25],"causal":[27,59,73,156],"case":[30],"binary":[32],"or":[33,44],"multinoulli":[34],"treatments.":[35,80,104],"These":[36],"models":[37,132],"are":[38,88],"either":[39],"incompatible":[40],"with":[41],"treatments,":[43],"extending":[45],"them":[46],"computationally":[51],"expensive.":[52],"We":[53],"use":[54],"previous":[56],"formulation":[57],"variational":[62],"autoencoder":[63],"(VAE)":[64],"and":[65,159],"propose":[66],"novel":[68],"architecture":[69],"estimate":[71],"effect":[74],"any":[76],"subset":[77,224],"The":[81,94,105,145,164,190,213,231],"higher":[82],"order":[83],"effects":[84],"captured":[89],"through":[90],"task":[92,95],"embedding.":[93],"embedding":[96],"allows":[97],"model":[99,106,126,178,192],"scale":[101],"applied":[108],"real":[110,143,234],"digital":[111],"marketing":[112,121],"dataset":[113],"evaluate":[115],"next":[117],"best":[118,204],"set":[119],"actions.":[122],"For":[123],"evaluation,":[124],"compared":[128,185],"against":[129],"competitive":[130],"baseline":[131,205],"two":[134],"semi-synthetic":[135],"datasets":[136],"created":[137],"covariates":[140],"from":[141],"dataset.":[144],"performance":[146],"measured":[148],"along":[149,196,209],"four":[150],"evaluation":[151,166,199,211],"metrics":[152],"considered":[153],"one":[160],"proposed":[161,165,191,214,247],"by":[162,206,245],"us.":[163],"metric":[167],"measures":[168],"loss":[170],"expected":[173],"outcome":[174],"when":[175,222],"particular":[177],"used":[180],"for":[181],"decision":[182],"making":[183],"as":[184],"ground":[188],"truth.":[189],"outperforms":[193,202],"baselines":[195],"all":[197],"five":[198],"metrics.":[200,212],"It":[201],"over":[207],"30%":[208],"these":[210],"approach":[215,243],"also":[217],"shown":[218],"be":[220],"robust":[221],"confounders":[227],"not":[229],"observed.":[230],"results":[232],"show":[236],"importance":[238],"flexible":[241],"modeling":[242],"provided":[244],"model.":[248]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W2911523443","counts_by_year":[{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":2}],"updated_date":"2024-11-22T14:40:45.840044","created_date":"2019-02-21"}