{"id":"https://openalex.org/W7133618820","doi":"https://doi.org/10.48550/arxiv.2603.03587","title":"Controllable Generative Sandbox for Causal Inference","display_name":"Controllable Generative Sandbox for Causal Inference","publication_year":2026,"publication_date":"2026-03-03","ids":{"openalex":"https://openalex.org/W7133618820","doi":"https://doi.org/10.48550/arxiv.2603.03587"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2603.03587","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"article","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5128191927","display_name":"Qi Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Qi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128180439","display_name":"Harsh Parikh","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Parikh, Harsh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089693948","display_name":"Ashley I. Naimi","orcid":"https://orcid.org/0000-0002-1510-8175"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Naimi, Ashley","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040156293","display_name":"Razieh Nabi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nabi, Razieh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128141562","display_name":"Christopher Kim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, Christopher","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5039413618","display_name":"Timothy L. Lash","orcid":"https://orcid.org/0000-0002-5240-5195"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lash, Timothy","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":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.27444665,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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":0.7602999806404114,"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":0.7602999806404114,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.04600000008940697,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.03959999978542328,"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/causal-inference","display_name":"Causal inference","score":0.7114999890327454},{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.6830999851226807},{"id":"https://openalex.org/keywords/causal-model","display_name":"Causal model","score":0.5903000235557556},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5212000012397766},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.4602999985218048},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.4505000114440918},{"id":"https://openalex.org/keywords/counterfactual-thinking","display_name":"Counterfactual thinking","score":0.43860000371932983},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.4115999937057495},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.40700000524520874},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.39969998598098755}],"concepts":[{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.7114999890327454},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.6830999851226807},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5999000072479248},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.5903000235557556},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5212000012397766},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5182999968528748},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49390000104904175},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.4602999985218048},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.4505000114440918},{"id":"https://openalex.org/C108650721","wikidata":"https://www.wikidata.org/wiki/Q1783253","display_name":"Counterfactual thinking","level":2,"score":0.43860000371932983},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.4115999937057495},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.40700000524520874},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.39969998598098755},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.36739999055862427},{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.35100001096725464},{"id":"https://openalex.org/C2779901538","wikidata":"https://www.wikidata.org/wiki/Q6816584","display_name":"Mendelian randomization","level":5,"score":0.34049999713897705},{"id":"https://openalex.org/C77350462","wikidata":"https://www.wikidata.org/wiki/Q1125472","display_name":"Confounding","level":2,"score":0.3319000005722046},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.32600000500679016},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3255000114440918},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.32019999623298645},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.3084000051021576},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.3010999858379364},{"id":"https://openalex.org/C155108698","wikidata":"https://www.wikidata.org/wiki/Q1231081","display_name":"Randomized experiment","level":2,"score":0.2953000068664551},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.2741999924182892},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.27239999175071716},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.2721000015735626},{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.2630000114440918},{"id":"https://openalex.org/C55166926","wikidata":"https://www.wikidata.org/wiki/Q2892946","display_name":"Oracle","level":2,"score":0.259799987077713},{"id":"https://openalex.org/C26831200","wikidata":"https://www.wikidata.org/wiki/Q16963953","display_name":"Marginal structural model","level":3,"score":0.2538999915122986},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.25360000133514404},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2526000142097473},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.25209999084472656},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.25040000677108765}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2603.03587","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2603.03587","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.03587","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":"pmh:doi:10.48550/arxiv.2603.03587","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"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":{"Method":[0],"validation":[1],"and":[2,26,40,69,90,113,166],"study":[3,148],"design":[4,122],"in":[5,144],"causal":[6,31,76,107,138],"inference":[7],"rely":[8],"on":[9,131],"synthetic":[10],"data":[11,101],"with":[12,63],"known":[13],"counterfactuals.":[14],"Existing":[15],"simulators":[16],"trade":[17],"off":[18],"distributional":[19,129],"realism,":[20],"the":[21,99],"ability":[22],"to":[23,98,117,157],"capture":[24],"mixed-type":[25,132],"multimodal":[27],"tabular":[28],"data,":[29],"against":[30],"controllability,":[32],"including":[33],"explicit":[34,75],"control":[35],"over":[36],"overlap,":[37,110],"unmeasured":[38],"confounding,":[39],"treatment":[41,114,173],"effect":[42,91,115,174],"heterogeneity.":[43,92],"We":[44,140],"introduce":[45],"CausalMix,":[46],"a":[47,57,145],"variational":[48],"generative":[49],"framework":[50],"that":[51],"closes":[52],"this":[53],"gap":[54],"by":[55],"coupling":[56],"mixture":[58],"of":[59,87,106,149],"Gaussian":[60],"latent":[61],"priors":[62],"data-type-specific":[64],"decoders":[65],"for":[66],"continuous,":[67],"binary,":[68],"categorical":[70],"variables.":[71],"The":[72],"model":[73],"incorporates":[74],"controls:":[77],"an":[78],"overlap":[79],"regularizer":[80],"shaping":[81],"propensity-score":[82],"distributions,":[83],"alongside":[84],"direct":[85],"parameterizations":[86],"confounding":[88,111],"strength":[89],"This":[93],"unified":[94],"objective":[95],"preserves":[96],"fidelity":[97],"observed":[100],"while":[102,134],"enabling":[103],"factorial":[104],"manipulation":[105],"mechanisms,":[108],"allowing":[109],"strength,":[112],"heterogeneity":[116,175],"be":[118],"varied":[119],"independently":[120],"at":[121],"time.":[123],"Across":[124],"benchmarks,":[125],"CausalMix":[126,156],"achieves":[127],"state-of-the-art":[128],"metrics":[130],"tables":[133],"providing":[135],"stable,":[136],"fine-grained":[137],"control.":[139],"demonstrate":[141],"practical":[142],"utility":[143],"comparative":[146],"safety":[147],"metastatic":[150],"castration-resistant":[151],"prostate":[152],"cancer":[153],"treatments,":[154],"using":[155],"compare":[158],"estimators":[159],"under":[160,171],"calibrated":[161],"data-generating":[162],"processes,":[163],"tune":[164],"hyperparameters,":[165],"conduct":[167],"simulation-based":[168],"power":[169],"analyses":[170],"targeted":[172],"scenarios.":[176]},"counts_by_year":[],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2026-03-06T00:00:00"}
