{"id":"https://openalex.org/W7160617948","doi":"https://doi.org/10.48550/arxiv.2605.06564","title":"Dynamic Treatment on Networks","display_name":"Dynamic Treatment on Networks","publication_year":2026,"publication_date":"2026-05-07","ids":{"openalex":"https://openalex.org/W7160617948","doi":"https://doi.org/10.48550/arxiv.2605.06564"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.06564","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.06564","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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.2605.06564","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135713012","display_name":"Bengusu Nar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nar, Bengusu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135696987","display_name":"Jiguang Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Jiguang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091109856","display_name":"Veronika Ro\u010dkov\u00e1","orcid":"https://orcid.org/0000-0002-0852-9393"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ro\u010dkov\u00e1, Veronika","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5050481354","display_name":"Panos Toulis","orcid":"https://orcid.org/0000-0002-0061-9984"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Toulis, Panos","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":0.8216000199317932,"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":0.8216000199317932,"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/T11886","display_name":"Agricultural risk and resilience","score":0.06780000030994415,"subfield":{"id":"https://openalex.org/subfields/1111","display_name":"Soil Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10987","display_name":"Microfinance and Financial Inclusion","score":0.02160000056028366,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/dynamic-bayesian-network","display_name":"Dynamic Bayesian network","score":0.598800003528595},{"id":"https://openalex.org/keywords/regret","display_name":"Regret","score":0.5311999917030334},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.5128999948501587},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.48069998621940613},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.4699999988079071},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4677000045776367},{"id":"https://openalex.org/keywords/dynamic-network-analysis","display_name":"Dynamic network analysis","score":0.4560999870300293},{"id":"https://openalex.org/keywords/centrality","display_name":"Centrality","score":0.42890000343322754},{"id":"https://openalex.org/keywords/dynamic-programming","display_name":"Dynamic programming","score":0.39410001039505005}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6974999904632568},{"id":"https://openalex.org/C82142266","wikidata":"https://www.wikidata.org/wiki/Q3456604","display_name":"Dynamic Bayesian network","level":3,"score":0.598800003528595},{"id":"https://openalex.org/C50817715","wikidata":"https://www.wikidata.org/wiki/Q79895177","display_name":"Regret","level":2,"score":0.5311999917030334},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.5128999948501587},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.48069998621940613},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.4699999988079071},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4677000045776367},{"id":"https://openalex.org/C13540734","wikidata":"https://www.wikidata.org/wiki/Q5318996","display_name":"Dynamic network analysis","level":2,"score":0.4560999870300293},{"id":"https://openalex.org/C53811970","wikidata":"https://www.wikidata.org/wiki/Q5062194","display_name":"Centrality","level":2,"score":0.42890000343322754},{"id":"https://openalex.org/C37404715","wikidata":"https://www.wikidata.org/wiki/Q380679","display_name":"Dynamic programming","level":2,"score":0.39410001039505005},{"id":"https://openalex.org/C2779982251","wikidata":"https://www.wikidata.org/wiki/Q25053762","display_name":"Stochastic block model","level":3,"score":0.3531999886035919},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.3366999924182892},{"id":"https://openalex.org/C123757187","wikidata":"https://www.wikidata.org/wiki/Q9195957","display_name":"Network dynamics","level":2,"score":0.2890999913215637},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.2883000075817108},{"id":"https://openalex.org/C124304363","wikidata":"https://www.wikidata.org/wiki/Q673661","display_name":"Abstraction","level":2,"score":0.28769999742507935},{"id":"https://openalex.org/C97256817","wikidata":"https://www.wikidata.org/wiki/Q1462316","display_name":"Spurious relationship","level":2,"score":0.28540000319480896},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2784000039100647},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.2766000032424927},{"id":"https://openalex.org/C31170391","wikidata":"https://www.wikidata.org/wiki/Q188619","display_name":"Hierarchy","level":2,"score":0.26829999685287476},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.2669000029563904},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.265500009059906},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.2597000002861023},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.25850000977516174},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.2556000053882599},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2533000111579895},{"id":"https://openalex.org/C34947359","wikidata":"https://www.wikidata.org/wiki/Q665189","display_name":"Complex network","level":2,"score":0.25189998745918274}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.06564","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.06564","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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.2605.06564","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.06564","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":[{"id":"https://metadata.un.org/sdg/8","score":0.7292598485946655,"display_name":"Decent work and economic growth"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0],"networks,":[1],"effective":[2],"dynamic":[3,54,81,103,116],"treatment":[4,45,55,91],"allocation":[5],"requires":[6],"deciding":[7],"both":[8],"whom":[9],"to":[10,17,154],"treat":[11],"and":[12,66,99,142,161,172],"also":[13],"when,":[14],"so":[15],"as":[16],"amplify":[18],"policy":[19,104],"impact":[20],"through":[21],"spillovers.":[22],"An":[23],"early":[24],"intervention":[25],"at":[26],"a":[27,69,79,85,102,128],"well-connected":[28],"node":[29],"can":[30],"trigger":[31],"cascades":[32],"that":[33,72,133,174],"change":[34],"which":[35],"nodes":[36],"are":[37,50],"worth":[38],"targeting":[39,176],"in":[40,146],"the":[41],"next":[42],"period.":[43],"Existing":[44],"strategies":[46],"under":[47,166],"network":[48,59,75,139],"interference":[49],"largely":[51],"static":[52,178],"while":[53],"frameworks":[56],"typically":[57],"ignore":[58],"structure":[60],"altogether.":[61],"We":[62,126,150],"integrate":[63],"these":[64],"perspectives":[65],"propose":[67],"Q-Ising,":[68],"three-stage":[70],"pipeline":[71],"(i)":[73],"estimates":[74],"adoption":[76,92],"dynamics":[77,171],"via":[78,105],"Bayesian":[80,110],"Ising":[82,147],"model":[83],"from":[84,156],"single":[86],"observed":[87],"panel,":[88],"(ii)":[89],"augments":[90],"histories":[93],"with":[94,122],"continuous":[95],"posterior":[96,119],"latent":[97],"states,":[98],"(iii)":[100],"learns":[101],"offline":[106],"reinforcement":[107],"learning.":[108],"The":[109],"mechanism":[111],"enables":[112],"uncertainty":[113],"quantification":[114],"over":[115],"decisions,":[117],"yielding":[118],"ensemble":[120],"policies":[121],"interpretable":[123],"spillover":[124],"estimates.":[125],"provide":[127],"finite-sample":[129],"regret":[130],"upper":[131],"bound":[132],"decomposes":[134],"into":[135],"standard":[136],"offline-RL":[137],"uncertainty,":[138],"abstraction":[140],"error,":[141],"first":[143],"stage":[144],"error":[145],"state":[148],"estimation.":[149],"apply":[151],"our":[152],"method":[153],"data":[155],"Indian":[157],"village":[158],"microfinance":[159],"networks":[160],"synthetic":[162],"stochastic":[163],"block":[164],"models":[165],"simulated":[167],"heterogeneous":[168],"susceptible-infected-susceptible":[169],"(SIS)":[170],"demonstrate":[173],"adaptive":[175],"outperforms":[177],"centrality":[179],"benchmarks.":[180]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-09T00:00:00"}