{"id":"https://openalex.org/W4205459532","doi":"https://doi.org/10.1109/bigdata52589.2021.9671439","title":"A Short Survey on Forest Based Heterogeneous Treatment Effect Estimation Methods: Meta-learners and Specific Models","display_name":"A Short Survey on Forest Based Heterogeneous Treatment Effect Estimation Methods: Meta-learners and Specific Models","publication_year":2021,"publication_date":"2021-12-15","ids":{"openalex":"https://openalex.org/W4205459532","doi":"https://doi.org/10.1109/bigdata52589.2021.9671439"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata52589.2021.9671439","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671439","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Big Data (Big Data)","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/A5041881347","display_name":"Hao Jiang","orcid":"https://orcid.org/0000-0002-6757-4231"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Hao Jiang","raw_affiliation_strings":["Instacart, San Francisco, CA, US"],"affiliations":[{"raw_affiliation_string":"Instacart, San Francisco, CA, US","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101682276","display_name":"Peng Qi","orcid":"https://orcid.org/0000-0003-0390-5449"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Peng Qi","raw_affiliation_strings":["Instacart, San Francisco, CA, US"],"affiliations":[{"raw_affiliation_string":"Instacart, San Francisco, CA, US","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111013278","display_name":"Jingying Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jingying Zhou","raw_affiliation_strings":["Instacart, San Francisco, CA, US"],"affiliations":[{"raw_affiliation_string":"Instacart, San Francisco, CA, US","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103443107","display_name":"Jack G. Zhou","orcid":"https://orcid.org/0009-0006-5475-0140"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jack Zhou","raw_affiliation_strings":["Instacart, San Francisco, CA, US"],"affiliations":[{"raw_affiliation_string":"Instacart, San Francisco, CA, US","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5104022986","display_name":"Sharath Rao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sharath Rao","raw_affiliation_strings":["Instacart, San Francisco, CA, US"],"affiliations":[{"raw_affiliation_string":"Instacart, San Francisco, CA, US","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5041881347"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.1109,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.88832487,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"3006","last_page":"3012"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10845","display_name":"Advanced Causal Inference Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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/T10136","display_name":"Statistical Methods and Inference","score":0.9962999820709229,"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/T11235","display_name":"Statistical Methods in Clinical Trials","score":0.9919999837875366,"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/computer-science","display_name":"Computer science","score":0.7144377827644348},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6842279434204102},{"id":"https://openalex.org/keywords/causal-inference","display_name":"Causal inference","score":0.6597565412521362},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6530845761299133},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.6205456852912903},{"id":"https://openalex.org/keywords/meta-learning","display_name":"Meta learning (computer science)","score":0.5578603744506836},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5477775931358337},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.4983038902282715},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4605258107185364},{"id":"https://openalex.org/keywords/causation","display_name":"Causation","score":0.4507749080657959},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.4260319471359253},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.4183562099933624},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3842296004295349},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.21149587631225586},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11277154088020325}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7144377827644348},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6842279434204102},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.6597565412521362},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6530845761299133},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6205456852912903},{"id":"https://openalex.org/C2781002164","wikidata":"https://www.wikidata.org/wiki/Q6822311","display_name":"Meta learning (computer science)","level":3,"score":0.5578603744506836},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5477775931358337},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.4983038902282715},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4605258107185364},{"id":"https://openalex.org/C166151441","wikidata":"https://www.wikidata.org/wiki/Q4923601","display_name":"Causation","level":2,"score":0.4507749080657959},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.4260319471359253},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.4183562099933624},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3842296004295349},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.21149587631225586},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11277154088020325},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","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/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata52589.2021.9671439","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671439","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1570649553","https://openalex.org/W1978108654","https://openalex.org/W2011485768","https://openalex.org/W2132917208","https://openalex.org/W2208550830","https://openalex.org/W2583860259","https://openalex.org/W2624816748","https://openalex.org/W2804691295","https://openalex.org/W2954308195","https://openalex.org/W2962727190","https://openalex.org/W2963371984","https://openalex.org/W3008689546","https://openalex.org/W3097704231","https://openalex.org/W3122128060","https://openalex.org/W3124999902","https://openalex.org/W3150893739","https://openalex.org/W4297957988","https://openalex.org/W6634190766","https://openalex.org/W6681551524","https://openalex.org/W6751846514","https://openalex.org/W6760347182","https://openalex.org/W6774054988","https://openalex.org/W6785379030"],"related_works":["https://openalex.org/W2372620761","https://openalex.org/W180255526","https://openalex.org/W2915678288","https://openalex.org/W133423432","https://openalex.org/W1937721613","https://openalex.org/W2614320617","https://openalex.org/W2497053598","https://openalex.org/W4253624503","https://openalex.org/W4388356905","https://openalex.org/W4255808401"],"abstract_inverted_index":{"Causation":[0],"is":[1,32,42],"gradually":[2],"paid":[3],"more":[4,26,64],"attention":[5],"to":[6,114,129,140],"in":[7,99],"industry":[8],"as":[9,95,149],"compared":[10],"with":[11,173],"correlation":[12],"statement,":[13],"it":[14,62],"straightly":[15],"targets":[16],"on":[17,37,53],"answering":[18],"what-if":[19],"questions,":[20],"which":[21,60],"generally":[22],"delivers":[23],"deeper":[24],"and":[25,57,105,125,152,163,177],"insightful":[27],"conclusions.":[28],"Therefore,":[29],"causal":[30,47,79],"inference":[31,48,80],"naturally":[33],"called.":[34],"Mainly":[35],"targeting":[36],"modeling":[38,58],"counter-factual":[39],"relationship":[40],"that":[41,144],"usually":[43],"not":[44],"directly":[45],"observable,":[46],"has":[49],"various":[50],"of":[51,74,108,137],"challenges":[52],"both":[54,142,174],"problem":[55],"setup":[56],"side,":[59],"makes":[61],"a":[63,96,106,131],"complex":[65],"topic":[66],"than":[67],"regular":[68],"supervised":[69],"learning":[70],"task.":[71],"As":[72],"one":[73],"the":[75,135],"heated":[76],"discussed":[77,160],"specific":[78,154,166],"problems,":[81],"conditional":[82],"average":[83],"treatment":[84,89],"effect":[85,90],"(CATE),":[86],"or":[87],"heterogeneous":[88],"(HTE),":[91],"estimation":[92],"model":[93,139],"serves":[94],"powerful":[97],"tool":[98],"many":[100],"applications,":[101],"like":[102,128],"personalized":[103],"medicine":[104],"series":[107],"uplift":[109],"problems":[110],"from":[111,134],"user":[112],"segmentation":[113],"ads":[115],"budget":[116],"optimization.":[117],"Recently,":[118],"several":[119],"new":[120],"CATE":[121,155],"methods":[122],"were":[123],"proposed":[124],"we":[126,159],"would":[127],"do":[130],"short":[132],"survey":[133],"perspective":[136],"forest-based":[138,153,165],"cover":[141],"meta-learners":[143,162],"could":[145],"take":[146],"random":[147],"forest":[148],"base":[150],"learner":[151],"models.":[156,167],"In":[157],"total,":[158],"7":[161],"5":[164],"We":[168],"empirically":[169],"evaluate":[170],"these":[171],"models":[172],"synthetic":[175],"data":[176],"real":[178],"dataset.":[179]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3}],"updated_date":"2025-11-25T21:42:39.735039","created_date":"2025-10-10T00:00:00"}