{"id":"https://openalex.org/W7161264558","doi":"https://doi.org/10.48550/arxiv.2605.14284","title":"Smooth Multi-Policy Causal Effect Estimation in Longitudinal Settings","display_name":"Smooth Multi-Policy Causal Effect Estimation in Longitudinal Settings","publication_year":2026,"publication_date":"2026-05-14","ids":{"openalex":"https://openalex.org/W7161264558","doi":"https://doi.org/10.48550/arxiv.2605.14284"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.14284","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.14284","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.14284","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136231932","display_name":"Wenxin Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Wenxin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136208964","display_name":"Weishen Pan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pan, Weishen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022172569","display_name":"Kyra Gan","orcid":"https://orcid.org/0000-0002-5147-8747"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gan, Kyra","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136234240","display_name":"Fei Wang","orcid":"https://orcid.org/0009-0009-7542-9569"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Fei","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":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10845","display_name":"Advanced Causal Inference Techniques","score":0.8726999759674072,"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":0.8726999759674072,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.04580000042915344,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.020999999716877937,"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/debiasing","display_name":"Debiasing","score":0.6294000148773193},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.5906999707221985},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.58160001039505},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5733000040054321},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5667999982833862},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.522599995136261},{"id":"https://openalex.org/keywords/causal-inference","display_name":"Causal inference","score":0.5188000202178955},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.47940000891685486},{"id":"https://openalex.org/keywords/conditional-independence","display_name":"Conditional independence","score":0.36820000410079956}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6309999823570251},{"id":"https://openalex.org/C2779458634","wikidata":"https://www.wikidata.org/wiki/Q24963715","display_name":"Debiasing","level":2,"score":0.6294000148773193},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.5906999707221985},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.58160001039505},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5733000040054321},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5667999982833862},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.522599995136261},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.5188000202178955},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.47940000891685486},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3720000088214874},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3711000084877014},{"id":"https://openalex.org/C79772020","wikidata":"https://www.wikidata.org/wiki/Q5159264","display_name":"Conditional independence","level":2,"score":0.36820000410079956},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.3418000042438507},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3379000127315521},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.321399986743927},{"id":"https://openalex.org/C186215838","wikidata":"https://www.wikidata.org/wiki/Q772232","display_name":"Conditional expectation","level":2,"score":0.30239999294281006},{"id":"https://openalex.org/C71134354","wikidata":"https://www.wikidata.org/wiki/Q458825","display_name":"Kernel density estimation","level":3,"score":0.2953000068664551},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.29190000891685486},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.28450000286102295},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.28369998931884766},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2822999954223633},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.2689000070095062},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.2680000066757202},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.2678999900817871},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2669000029563904},{"id":"https://openalex.org/C2777472644","wikidata":"https://www.wikidata.org/wiki/Q16968992","display_name":"Approximate inference","level":3,"score":0.2660999894142151},{"id":"https://openalex.org/C29202148","wikidata":"https://www.wikidata.org/wiki/Q287260","display_name":"Resource allocation","level":2,"score":0.26420000195503235},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.2603999972343445},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.25209999084472656}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.14284","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.14284","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.14284","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.14284","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Comparative":[0],"evaluation":[1],"of":[2,63],"multiple":[3],"dynamic":[4],"treatment":[5],"policies":[6],"is":[7,96],"essential":[8],"for":[9],"healthcare":[10],"and":[11],"policy":[12,92,110],"decisions,":[13],"yet":[14],"conventional":[15],"longitudinal":[16,50],"causal":[17],"inference":[18],"methods":[19],"estimate":[20],"each":[21],"in":[22,80,149],"isolation,":[23],"preventing":[24],"information":[25],"sharing":[26],"across":[27],"counterfactuals.":[28],"We":[29,76],"demonstrate":[30,138],"that":[31,69,103,139],"this":[32,78,120],"separate":[33],"estimation":[34,72],"paradigm":[35],"induces":[36],"a":[37,60,90,123],"structurally":[38],"uncontrolled":[39],"second-order":[40,128],"bias,":[41],"inflating":[42],"finite-sample":[43,132],"variance":[44],"even":[45],"after":[46],"standard":[47],"debiasing":[48],"with":[49],"targeted":[51],"maximum":[52],"likelihood":[53],"estimation(LTMLE).":[54],"To":[55],"address":[56],"this,":[57],"we":[58,118],"propose":[59],"policy-aware":[61],"reparameterization":[62],"Iterative":[64],"Conditional":[65],"Expectation":[66],"(ICE)":[67],"Q-functions":[68],"enables":[70],"joint":[71],"through":[73],"shared":[74,91],"representations.":[75],"implement":[77],"approach":[79],"the":[81,104,127],"Policy-Encoded":[82],"Q":[83],"Network":[84],"(PEQ-Net),":[85],"an":[86,114],"architecture":[87],"centered":[88],"on":[89,126,135],"encoder.":[93],"The":[94],"encoder":[95],"trained":[97],"using":[98],"kernel":[99],"mean":[100],"embeddings,":[101],"ensuring":[102],"learned":[105],"representation":[106],"space":[107],"reflects":[108],"population-level":[109],"dissimilarities.":[111],"After":[112],"applying":[113],"LTMLE":[115],"correction":[116],"step,":[117],"prove":[119],"design":[121],"imposes":[122],"structural":[124],"constraint":[125],"remainder,":[129],"thereby":[130],"stabilizing":[131],"variance.":[133],"Experiments":[134],"semi-synthetic":[136],"datasets":[137],"PEQ-Net":[140],"consistently":[141],"outperforms":[142],"existing":[143],"ICE-based":[144],"methods,":[145],"achieving":[146],"substantial":[147],"reductions":[148],"root-mean-square":[150],"error,":[151],"particularly":[152],"when":[153],"evaluating":[154],"closely":[155],"related":[156],"policies.":[157]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-16T00:00:00"}