{"id":"https://openalex.org/W7215125752","doi":"https://doi.org/10.48550/arxiv.2609.38692","title":"Imputation is all you need: double robustness, semiparametric efficiency, and automatic covariate balance for estimating the average treatment effect","display_name":"Imputation is all you need: double robustness, semiparametric efficiency, and automatic covariate balance for estimating the average treatment effect","publication_year":2026,"publication_date":"2026-09-30","ids":{"openalex":"https://openalex.org/W7215125752","doi":"https://doi.org/10.48550/arxiv.2609.38692"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2609.38692","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.38692","pdf_url":"https://arxiv.org/pdf/2609.38692","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2609.38692","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5021528850","display_name":"Fang Han","orcid":"https://orcid.org/0000-0003-2996-5693","observed_orcids":["https://orcid.org/0000-0003-2996-5693"]},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Han, Fang","raw_affiliation_strings":["Department of Statistics , University of Washington , Seattle , WA 98195 , USA;"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics , University of Washington , Seattle , WA 98195 , USA;","institution_ids":["https://openalex.org/I201448701"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101550145","display_name":"Peng Ding","orcid":"https://orcid.org/0000-0002-2704-2353","observed_orcids":["https://orcid.org/0000-0002-2704-2353"]},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ding, Peng","raw_affiliation_strings":["Department of Statistics , University of California , Berkeley , CA 94720 , USA;"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics , University of California , Berkeley , CA 94720 , USA;","institution_ids":["https://openalex.org/I95457486"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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":0.9994999766349792,"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":0.9994999766349792,"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/T10136","display_name":"Statistical Methods and Inference","score":9.999999747378752e-05,"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/T10243","display_name":"Statistical Methods and Bayesian Inference","score":9.999999747378752e-05,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/double-robustness","display_name":"double robustness","score":0.828000009059906},{"id":"https://openalex.org/keywords/semiparametric-efficiency","display_name":"semiparametric efficiency","score":0.9679999947547913},{"id":"https://openalex.org/keywords/average-treatment-effect","display_name":"average treatment effect","score":0.7459999918937683},{"id":"https://openalex.org/keywords/covariate-balance","display_name":"covariate balance","score":0.8539999723434448},{"id":"https://openalex.org/keywords/efficient-influence-function","display_name":"efficient influence function","score":0.847000002861023},{"id":"https://openalex.org/keywords/causal-inference","display_name":"causal inference","score":0.5289999842643738},{"id":"https://openalex.org/keywords/l2-projection","display_name":"L2 projection","score":0.597000002861023}],"concepts":[{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.7894999980926514},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.7871000170707703},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.692799985408783},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.6693999767303467},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.5931000113487244},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.5354999899864197},{"id":"https://openalex.org/C17732976","wikidata":"https://www.wikidata.org/wiki/Q7511782","display_name":"Sieve (category theory)","level":2,"score":0.5152999758720398},{"id":"https://openalex.org/C2779915747","wikidata":"https://www.wikidata.org/wiki/Q17058619","display_name":"Inverse probability weighting","level":3,"score":0.5041999816894531},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.49059998989105225},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.489300012588501},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4878999888896942},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4772999882698059},{"id":"https://openalex.org/C89337504","wikidata":"https://www.wikidata.org/wiki/Q4828276","display_name":"Average treatment effect","level":3,"score":0.35530000925064087},{"id":"https://openalex.org/C19539793","wikidata":"https://www.wikidata.org/wiki/Q7449609","display_name":"Semiparametric regression","level":3,"score":0.3319999873638153},{"id":"https://openalex.org/C204016326","wikidata":"https://www.wikidata.org/wiki/Q5401384","display_name":"Estimating equations","level":3,"score":0.31119999289512634},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.2964000105857849},{"id":"https://openalex.org/C142259097","wikidata":"https://www.wikidata.org/wiki/Q5891314","display_name":"Homogeneity (statistics)","level":2,"score":0.2815000116825104},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.2732999920845032},{"id":"https://openalex.org/C65778772","wikidata":"https://www.wikidata.org/wiki/Q12345341","display_name":"Asymptotic distribution","level":3,"score":0.2720000147819519},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.27140000462532043},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.2648000121116638},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.2590999901294708},{"id":"https://openalex.org/C70136482","wikidata":"https://www.wikidata.org/wiki/Q13583781","display_name":"A-weighting","level":3,"score":0.2556000053882599},{"id":"https://openalex.org/C2983643213","wikidata":"https://www.wikidata.org/wiki/Q6030225","display_name":"Influence function","level":3,"score":0.25119999051094055}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2609.38692","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.38692","pdf_url":"https://arxiv.org/pdf/2609.38692","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2609.38692","doi":"https://doi.org/10.48550/arxiv.2609.38692","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2609.38692","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,"listed_in":[],"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"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2609.38692","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.38692","pdf_url":"https://arxiv.org/pdf/2609.38692","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7215125752.pdf","grobid_xml":"https://content.openalex.org/works/W7215125752.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4413618532","https://openalex.org/W4401026257","https://openalex.org/W7100917560","https://openalex.org/W2614056925","https://openalex.org/W1999188374","https://openalex.org/W2900200489","https://openalex.org/W7095440091","https://openalex.org/W2127699081","https://openalex.org/W3181001361","https://openalex.org/W2275620875"],"abstract_inverted_index":{"Imputation-based":[0],"causal":[1],"estimation":[2],"is":[3,21],"typically":[4],"viewed":[5],"as":[6],"relying":[7],"exclusively":[8],"on":[9],"an":[10,61,99],"outcome":[11,51],"model,":[12],"in":[13,44,71],"contrast":[14],"to":[15],"augmented":[16],"inverse-probability":[17],"weighting,":[18,121],"whose":[19,65],"consistency":[20],"protected":[22],"by":[23,37],"fitting":[24],"two":[25],"nuisance":[26],"models.":[27],"This":[28,95],"paper":[29],"argues":[30],"that":[31],"this":[32],"view":[33],"can":[34,129],"be":[35],"misleading":[36],"highlighting":[38],"a":[39],"hidden":[40],"dual":[41],"weighting":[42,63,79],"structure":[43,97],"least-squares":[45,139],"sieve":[46,73,93,108],"regression":[47],"imputation.":[48],"Although":[49],"only":[50],"regressions":[52],"are":[53,81],"explicitly":[54],"fitted,":[55],"the":[56,72,76,82,86,91,116,136],"resulting":[57],"imputation":[58,133],"estimator":[59],"admits":[60],"exact":[62],"representation":[64],"induced":[66],"weights":[67],"balance":[68],"every":[69],"function":[70],"space":[74],"and":[75,110,126],"corresponding":[77],"population":[78],"functions":[80],"$L^2$":[83],"projections":[84],"of":[85,102,138],"inverse":[87],"propensity":[88],"scores":[89],"onto":[90],"same":[92],"space.":[94],"projection":[96],"yields":[98],"implicit":[100],"form":[101],"double":[103,124],"robustness":[104],"and,":[105],"under":[106],"standard":[107],"approximation":[109],"growth":[111],"conditions,":[112],"asymptotic":[113],"linearity":[114],"with":[115],"efficient":[117],"influence":[118],"function.":[119],"Thus,":[120],"covariate":[122],"balance,":[123],"robustness,":[125],"semiparametric":[127],"efficiency":[128],"all":[130],"emerge":[131],"from":[132],"alone":[134],"through":[135],"geometry":[137],"projection.":[140]},"counts_by_year":[],"updated_date":"2026-11-06T12:05:46.655746","created_date":"2026-10-02T00:00:00"}