{"id":"https://openalex.org/W4247816235","doi":"https://doi.org/10.1109/asonam.2014.6921651","title":"Adjustments to propensity score matching for network structures","display_name":"Adjustments to propensity score matching for network structures","publication_year":2014,"publication_date":"2014-08-01","ids":{"openalex":"https://openalex.org/W4247816235","doi":"https://doi.org/10.1109/asonam.2014.6921651"},"language":"en","primary_location":{"id":"doi:10.1109/asonam.2014.6921651","is_oa":false,"landing_page_url":"https://doi.org/10.1109/asonam.2014.6921651","pdf_url":null,"source":{"id":"https://openalex.org/S4363608209","display_name":"2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)","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":"2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)","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/A5046710867","display_name":"Masoud Charkhabi","orcid":null},"institutions":[{"id":"https://openalex.org/I4210120376","display_name":"Canadian Imperial Bank of Commerce (Canada)","ror":"https://ror.org/02r0ks990","country_code":"CA","type":"company","lineage":["https://openalex.org/I4210120376"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Masoud Charkhabi","raw_affiliation_strings":["Advanced Analytics, Canadian Imperial Bank of Commerce, Toronto, Ontario"],"affiliations":[{"raw_affiliation_string":"Advanced Analytics, Canadian Imperial Bank of Commerce, Toronto, Ontario","institution_ids":["https://openalex.org/I4210120376"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5046710867"],"corresponding_institution_ids":["https://openalex.org/I4210120376"],"apc_list":null,"apc_paid":null,"fwci":0.9167,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.81481481,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"17","issue":null,"first_page":"628","last_page":"633"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10845","display_name":"Advanced Causal Inference Techniques","score":0.9958999752998352,"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":0.9958999752998352,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.9865999817848206,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13051","display_name":"Qualitative Comparative Analysis Research","score":0.9768999814987183,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/propensity-score-matching","display_name":"Propensity score matching","score":0.8362950086593628},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6729679703712463},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6244836449623108},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5627331733703613},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.546217679977417},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5145992040634155},{"id":"https://openalex.org/keywords/observational-study","display_name":"Observational study","score":0.45729103684425354},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.42508167028427124},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.41041702032089233},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40016984939575195},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3785129487514496},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.19944557547569275},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1351470947265625},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.12454560399055481}],"concepts":[{"id":"https://openalex.org/C17923572","wikidata":"https://www.wikidata.org/wiki/Q7250160","display_name":"Propensity score matching","level":2,"score":0.8362950086593628},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6729679703712463},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6244836449623108},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5627331733703613},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.546217679977417},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5145992040634155},{"id":"https://openalex.org/C23131810","wikidata":"https://www.wikidata.org/wiki/Q818574","display_name":"Observational study","level":2,"score":0.45729103684425354},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.42508167028427124},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.41041702032089233},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40016984939575195},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3785129487514496},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.19944557547569275},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1351470947265625},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.12454560399055481},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"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/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/asonam.2014.6921651","is_oa":false,"landing_page_url":"https://doi.org/10.1109/asonam.2014.6921651","pdf_url":null,"source":{"id":"https://openalex.org/S4363608209","display_name":"2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)","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":"2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Climate action","score":0.47999998927116394,"id":"https://metadata.un.org/sdg/13"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1435450741","https://openalex.org/W1578152639","https://openalex.org/W1972910909","https://openalex.org/W1979104937","https://openalex.org/W1999586185","https://openalex.org/W2007331509","https://openalex.org/W2036193982","https://openalex.org/W2102546764","https://openalex.org/W2117088188","https://openalex.org/W2117605350","https://openalex.org/W2122423197","https://openalex.org/W2149910108","https://openalex.org/W2150291618","https://openalex.org/W2154454189","https://openalex.org/W2187385045","https://openalex.org/W3121574798","https://openalex.org/W4298013486","https://openalex.org/W4298081798"],"related_works":["https://openalex.org/W2026576563","https://openalex.org/W2795791984","https://openalex.org/W2917189641","https://openalex.org/W4312375361","https://openalex.org/W2396000345","https://openalex.org/W4385077270","https://openalex.org/W71670329","https://openalex.org/W4281286690","https://openalex.org/W2040468604","https://openalex.org/W2111245922"],"abstract_inverted_index":{"Causal":[0],"inference":[1,34],"from":[2,128,167],"observational":[3],"data":[4,50,127],"rely":[5],"on":[6],"similar":[7],"treatment":[8,159],"and":[9,32,64,86,195,220],"control":[10],"groups":[11],"to":[12,18,22,38,103,125,137,152,171],"isolate":[13],"for":[14,24,40,191],"variation,":[15],"in":[16,20,47,109,193,210],"addition":[17],"adjustments":[19],"estimates":[21],"account":[23],"the":[25,48,52,66,76,79,117,141,150,158,161,175,198,215],"remaining":[26],"uncontrollable":[27],"variation.":[28],"Propensity":[29,118,142],"score":[30,84,177],"matching":[31,85],"statistical":[33],"are":[35,94,222],"established":[36],"tools":[37],"achieve":[39],"these":[41],"two":[42],"requirements":[43],"respectively.":[44],"Network":[45,144],"structures":[46],"underlying":[49],"of":[51,61,68,81,160,188,200,217],"experiment":[53],"challenge":[54],"this":[55,72,211],"convention":[56],"since":[57],"they":[58],"question":[59],"assumptions":[60],"independent":[62],"observations":[63],"increase":[65],"risk":[67],"unobserved":[69],"variables.":[70],"In":[71],"paper":[73],"we":[74],"approach":[75,124],"problem":[77],"with":[78,96,157],"intent":[80],"preserving":[82],"propensity":[83,176],"inference,":[87],"while":[88],"accommodating":[89],"network":[90,97,138,154,168,203],"information.":[91,98],"Multiple":[92],"experiments":[93,100],"re-evaluated":[95],"All":[99],"were":[101],"intended":[102],"create":[104],"organic":[105],"growth":[106],"through":[107],"referrals":[108],"a":[110,121,146,186],"financial":[111],"services":[112],"business.":[113],"We":[114,183,213],"offer":[115],"first,":[116],"Score":[119,143],"Layout;":[120],"rapid":[122],"visualization":[123],"scan":[126],"multiple":[129],"studies":[130],"that":[131,148,170],"potentially":[132],"may":[133],"require":[134],"re-evaluation":[135],"due":[136],"structure.":[139],"Second,":[140],"Risk;":[145],"metric":[147],"captures":[149],"extent":[151],"which":[153],"structure":[155],"interferes":[156],"experiment.":[162],"And":[163],"third;":[164],"variables":[165],"constructed":[166],"information":[169],"our":[172,201],"surprise":[173],"estimate":[174],"significantly":[178],"better":[179],"than":[180],"node":[181],"attributes.":[182],"also":[184],"present":[185],"set":[187],"interesting":[189],"problems":[190],"researchers":[192],"academia":[194],"industry.":[196],"To":[197],"best":[199],"knowledge":[202],"methods":[204],"have":[205],"not":[206],"been":[207],"studied":[208],"thoroughly":[209],"domain.":[212],"feel":[214],"combination":[216],"technique,":[218],"results":[219],"domain":[221],"novel.":[223]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}