{"id":"https://openalex.org/W7155561123","doi":"https://doi.org/10.1145/3799830.3799864","title":"The Rhythm of Treatment: Modeling Causal Effects Over Time With Marginal Structural Models and ODEs","display_name":"The Rhythm of Treatment: Modeling Causal Effects Over Time With Marginal Structural Models and ODEs","publication_year":2025,"publication_date":"2025-12-17","ids":{"openalex":"https://openalex.org/W7155561123","doi":"https://doi.org/10.1145/3799830.3799864"},"language":null,"primary_location":{"id":"doi:10.1145/3799830.3799864","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3799830.3799864","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 13th ACM IKDD International Conference on Data Science","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3799830.3799864","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134544917","display_name":"Shishir S Doddamani","orcid":"https://orcid.org/0009-0003-8979-394X"},"institutions":[{"id":"https://openalex.org/I98389781","display_name":"Dhirubhai Ambani Institute of Information and Communication Technology","ror":"https://ror.org/02d5b7g69","country_code":"IN","type":"education","lineage":["https://openalex.org/I98389781"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Shishir S Doddamani","raw_affiliation_strings":["Dhirubhai Ambani University, Gandhinagar, Gujarat, India"],"raw_orcid":"https://orcid.org/0009-0003-8979-394X","affiliations":[{"raw_affiliation_string":"Dhirubhai Ambani University, Gandhinagar, Gujarat, India","institution_ids":["https://openalex.org/I98389781"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5134471620","display_name":"Murthy Mittinty","orcid":"https://orcid.org/0000-0001-8778-9793"},"institutions":[{"id":"https://openalex.org/I169541294","display_name":"Flinders University","ror":"https://ror.org/01kpzv902","country_code":"AU","type":"education","lineage":["https://openalex.org/I169541294"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Murthy Mittinty","raw_affiliation_strings":["Flinders University, Adelaide, Australia"],"raw_orcid":"https://orcid.org/0000-0001-8778-9793","affiliations":[{"raw_affiliation_string":"Flinders University, Adelaide, Australia","institution_ids":["https://openalex.org/I169541294"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5134544917"],"corresponding_institution_ids":["https://openalex.org/I98389781"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.74297447,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"396","last_page":"401"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10560","display_name":"Diabetes Management and Research","score":0.5116999745368958,"subfield":{"id":"https://openalex.org/subfields/2712","display_name":"Endocrinology, Diabetes and Metabolism"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10560","display_name":"Diabetes Management and Research","score":0.5116999745368958,"subfield":{"id":"https://openalex.org/subfields/2712","display_name":"Endocrinology, Diabetes and Metabolism"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10845","display_name":"Advanced Causal Inference Techniques","score":0.1462000012397766,"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":0.08470000326633453,"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/ode","display_name":"Ode","score":0.6360999941825867},{"id":"https://openalex.org/keywords/propensity-score-matching","display_name":"Propensity score matching","score":0.539900004863739},{"id":"https://openalex.org/keywords/confounding","display_name":"Confounding","score":0.5189999938011169},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.46700000762939453},{"id":"https://openalex.org/keywords/causal-inference","display_name":"Causal inference","score":0.43380001187324524},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.40369999408721924},{"id":"https://openalex.org/keywords/average-treatment-effect","display_name":"Average treatment effect","score":0.3709999918937683},{"id":"https://openalex.org/keywords/hypoglycemia","display_name":"Hypoglycemia","score":0.34209999442100525}],"concepts":[{"id":"https://openalex.org/C34862557","wikidata":"https://www.wikidata.org/wiki/Q178985","display_name":"Ode","level":2,"score":0.6360999941825867},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5895000100135803},{"id":"https://openalex.org/C17923572","wikidata":"https://www.wikidata.org/wiki/Q7250160","display_name":"Propensity score matching","level":2,"score":0.539900004863739},{"id":"https://openalex.org/C77350462","wikidata":"https://www.wikidata.org/wiki/Q1125472","display_name":"Confounding","level":2,"score":0.5189999938011169},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.46700000762939453},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.43380001187324524},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.40369999408721924},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3896999955177307},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.375},{"id":"https://openalex.org/C89337504","wikidata":"https://www.wikidata.org/wiki/Q4828276","display_name":"Average treatment effect","level":3,"score":0.3709999918937683},{"id":"https://openalex.org/C2780668416","wikidata":"https://www.wikidata.org/wiki/Q202758","display_name":"Hypoglycemia","level":3,"score":0.34209999442100525},{"id":"https://openalex.org/C162144332","wikidata":"https://www.wikidata.org/wiki/Q1665305","display_name":"Instrumental variable","level":2,"score":0.31619998812675476},{"id":"https://openalex.org/C135343436","wikidata":"https://www.wikidata.org/wiki/Q170406","display_name":"Rhythm","level":2,"score":0.30820000171661377},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.30570000410079956},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.30559998750686646},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.29179999232292175},{"id":"https://openalex.org/C27403532","wikidata":"https://www.wikidata.org/wiki/Q5532474","display_name":"Generalized estimating equation","level":2,"score":0.289000004529953},{"id":"https://openalex.org/C26831200","wikidata":"https://www.wikidata.org/wiki/Q16963953","display_name":"Marginal structural model","level":3,"score":0.28299999237060547},{"id":"https://openalex.org/C99656134","wikidata":"https://www.wikidata.org/wiki/Q2912993","display_name":"Ordinary least squares","level":2,"score":0.267300009727478},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.2596000134944916},{"id":"https://openalex.org/C2777180221","wikidata":"https://www.wikidata.org/wiki/Q3025883","display_name":"Type 2 diabetes","level":3,"score":0.2572999894618988},{"id":"https://openalex.org/C2780353609","wikidata":"https://www.wikidata.org/wiki/Q1569064","display_name":"Artificial pancreas","level":4,"score":0.2542000114917755}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3799830.3799864","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3799830.3799864","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 13th ACM IKDD International Conference on Data Science","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3799830.3799864","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3799830.3799864","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 13th ACM IKDD International Conference on Data Science","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1622753297","https://openalex.org/W1964693647","https://openalex.org/W2090077024","https://openalex.org/W2111078766","https://openalex.org/W2139529156","https://openalex.org/W2239232218","https://openalex.org/W2724451718","https://openalex.org/W2980234472","https://openalex.org/W3167277098","https://openalex.org/W3175175052","https://openalex.org/W3210902789","https://openalex.org/W4392251501","https://openalex.org/W4399035719"],"related_works":[],"abstract_inverted_index":{"Treatment":[0],"in":[1],"everyday":[2],"care":[3],"is":[4,36,40,86],"reactive:":[5],"doses":[6],"change":[7],"because":[8,34],"patients":[9],"change,":[10],"creating":[11],"time-varying":[12],"confounding.":[13],"Purely":[14],"predictive":[15],"models":[16],"can":[17,103],"fit":[18,144],"well,":[19],"yet":[20],"encode":[21],"the":[22,43,106,119,142,156,166,177],"wrong":[23],"causal":[24],"story(e.g.":[25],"appearing":[26],"to":[27,64],"show":[28],"that":[29],"insulin":[30,35],"raises":[31],"glucose":[32,39,69,136,173],"simply":[33],"given":[37,89],"when":[38],"high).":[41],"At":[42],"population":[44,178],"level,":[45],"we":[46,95,117],"identify":[47],"marginal":[48],"effects":[49],"using":[50],"stabilized":[51],"generalized":[52,79],"propensity":[53,80],"score":[54],"weights":[55],"and":[56,75,137,148,153,171],"then":[57],"learn":[58,96],"a":[59,97],"sparse":[60,98],"ODE":[61,99],"with":[62,123],"SINDy":[63],"produce":[65],"interpretable,":[66],"deconfounded":[67,172],"short-horizon":[68,151],"predictions":[70,175],"from":[71],"free-living":[72],"CGM,":[73,114],"insulin,":[74,115],"meal":[76],"data.":[77],"Stabilized":[78],"scores":[81],"reweight":[82],"observations":[83],"so":[84],"treatment":[85],"as-if":[87],"randomized":[88],"history;":[90],"on":[91],"this":[92],"adjusted":[93],"signal,":[94],"whose":[100],"terms":[101],"clinicians":[102],"inspect.":[104],"Using":[105],"public":[107],"HUPA\u2013UCM":[108],"Type":[109],"1":[110],"diabetes":[111],"dataset":[112],"(5-minute":[113],"carbohydrates),":[116],"compare":[118],"equations":[120,168],"learned":[121,125,167],"directly":[122],"those":[124],"after":[126],"MSM":[127],"adjustment.":[128],"After":[129],"weighting,":[130],"signs":[131],"are":[132],"physiologically":[133],"coherent\u2014boluses":[134],"reduce":[135],"meals":[138],"increase":[139],"it":[140],"while":[141],"unweighted":[143],"shows":[145],"indication":[146],"bias":[147],"achieves":[149],"lower":[150],"RMSE":[152],"MAE":[154],"than":[155],"unadjusted":[157],"baseline,":[158],"which":[159],"reflects":[160],"confounding":[161],"by":[162],"indication.":[163],"In":[164],"summary,":[165],"yield":[169],"interpretable":[170],"trajectory":[174],"at":[176],"level.":[179]},"counts_by_year":[],"updated_date":"2026-04-25T06:06:54.107920","created_date":"2026-04-25T00:00:00"}