{"id":"https://openalex.org/W4409150376","doi":"https://doi.org/10.1145/3690624.3709176","title":"CSPI-MT: Calibrated Safe Policy Improvement with Multiple Testing for Threshold Policies","display_name":"CSPI-MT: Calibrated Safe Policy Improvement with Multiple Testing for Threshold Policies","publication_year":2025,"publication_date":"2025-04-04","ids":{"openalex":"https://openalex.org/W4409150376","doi":"https://doi.org/10.1145/3690624.3709176"},"language":"en","primary_location":{"id":"doi:10.1145/3690624.3709176","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3690624.3709176","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5010562025","display_name":"B. Cho","orcid":"https://orcid.org/0000-0003-3558-0415"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Brian Cho","raw_affiliation_strings":["Cornell University, New York, NY, USA"],"raw_orcid":"https://orcid.org/0000-0003-3558-0415","affiliations":[{"raw_affiliation_string":"Cornell University, New York, NY, USA","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085991817","display_name":"Ana-Roxana Pop","orcid":"https://orcid.org/0000-0003-3482-2734"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ana-Roxana Pop","raw_affiliation_strings":["Meta, New York, NY, USA"],"raw_orcid":"https://orcid.org/0000-0003-3482-2734","affiliations":[{"raw_affiliation_string":"Meta, New York, NY, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022172569","display_name":"Kyra Gan","orcid":"https://orcid.org/0000-0002-5147-8747"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kyra Gan","raw_affiliation_strings":["Cornell University, New York, NY, USA"],"raw_orcid":"https://orcid.org/0000-0002-5147-8747","affiliations":[{"raw_affiliation_string":"Cornell University, New York, NY, USA","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115573434","display_name":"Sam Corbett-Davies","orcid":"https://orcid.org/0000-0002-3849-2317"},"institutions":[{"id":"https://openalex.org/I4210099336","display_name":"Menlo School","ror":"https://ror.org/01240pn49","country_code":"US","type":"education","lineage":["https://openalex.org/I4210099336"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sam Corbett-Davies","raw_affiliation_strings":["Meta, Menlo Park, CA, USA"],"raw_orcid":"https://orcid.org/0000-0002-3849-2317","affiliations":[{"raw_affiliation_string":"Meta, Menlo Park, CA, USA","institution_ids":["https://openalex.org/I4210099336"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059111932","display_name":"Israel Nir","orcid":"https://orcid.org/0000-0001-8698-4730"},"institutions":[{"id":"https://openalex.org/I4210099336","display_name":"Menlo School","ror":"https://ror.org/01240pn49","country_code":"US","type":"education","lineage":["https://openalex.org/I4210099336"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Israel Nir","raw_affiliation_strings":["Meta, Menlo Park, CA, USA"],"raw_orcid":"https://orcid.org/0000-0001-8698-4730","affiliations":[{"raw_affiliation_string":"Meta, Menlo Park, CA, USA","institution_ids":["https://openalex.org/I4210099336"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115573435","display_name":"Ariel Evnine","orcid":null},"institutions":[{"id":"https://openalex.org/I4210099336","display_name":"Menlo School","ror":"https://ror.org/01240pn49","country_code":"US","type":"education","lineage":["https://openalex.org/I4210099336"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ariel Evnine","raw_affiliation_strings":["Meta, Menlo Park, CA, USA"],"raw_orcid":"https://orcid.org/0009-0007-5562-1962","affiliations":[{"raw_affiliation_string":"Meta, Menlo Park, CA, USA","institution_ids":["https://openalex.org/I4210099336"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036921114","display_name":"Nathan Kallus","orcid":"https://orcid.org/0000-0003-1672-0507"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]},{"id":"https://openalex.org/I869089601","display_name":"Netflix (United States)","ror":"https://ror.org/0197qw696","country_code":"US","type":"company","lineage":["https://openalex.org/I869089601"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nathan Kallus","raw_affiliation_strings":["Cornell University &amp; Netflix, New York, NY, USA"],"raw_orcid":"https://orcid.org/0000-0003-1672-0507","affiliations":[{"raw_affiliation_string":"Cornell University &amp; Netflix, New York, NY, USA","institution_ids":["https://openalex.org/I869089601","https://openalex.org/I205783295"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5010562025"],"corresponding_institution_ids":["https://openalex.org/I205783295"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.06578222,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"165","last_page":"176"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10845","display_name":"Advanced Causal Inference Techniques","score":0.9807999730110168,"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":0.9807999730110168,"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/T12423","display_name":"Software Reliability and Analysis Research","score":0.974399983882904,"subfield":{"id":"https://openalex.org/subfields/1712","display_name":"Software"},"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/T10136","display_name":"Statistical Methods and Inference","score":0.949400007724762,"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/computer-science","display_name":"Computer science","score":0.5898511409759521},{"id":"https://openalex.org/keywords/reliability-engineering","display_name":"Reliability engineering","score":0.39539268612861633},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09170138835906982}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5898511409759521},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.39539268612861633},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09170138835906982}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3690624.3709176","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3690624.3709176","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5,"id":"https://metadata.un.org/sdg/13","display_name":"Climate action"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W1551424636","https://openalex.org/W1974010676","https://openalex.org/W2071333295","https://openalex.org/W2120346329","https://openalex.org/W2132917208","https://openalex.org/W2161893832","https://openalex.org/W2161966552","https://openalex.org/W2169186423","https://openalex.org/W2418024629","https://openalex.org/W2559655401","https://openalex.org/W2560740065","https://openalex.org/W2793124255","https://openalex.org/W2890118063","https://openalex.org/W2891857078","https://openalex.org/W2963044034","https://openalex.org/W2963836326","https://openalex.org/W2969337892","https://openalex.org/W2995572947","https://openalex.org/W2997591727","https://openalex.org/W2999905431","https://openalex.org/W3035965352","https://openalex.org/W3046642824","https://openalex.org/W3093206925","https://openalex.org/W3099878876","https://openalex.org/W3119520891","https://openalex.org/W3123686390","https://openalex.org/W3204471946","https://openalex.org/W4226441234","https://openalex.org/W4286967777","https://openalex.org/W4288283381","https://openalex.org/W4289597633","https://openalex.org/W4292954448","https://openalex.org/W4301131730","https://openalex.org/W4317767734","https://openalex.org/W4391766500","https://openalex.org/W6640490175","https://openalex.org/W6782560597"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"When":[0],"modifying":[1],"existing":[2],"policies":[3,74,204],"in":[4,77,122,142,164],"high-risk":[5],"settings,":[6,144],"it":[7,49],"is":[8,50],"often":[9,101],"necessary":[10],"to":[11,52,105,108,131,158,179],"ensure":[12],"with":[13,59,75],"high":[14],"certainty":[15],"that":[16,141,194],"the":[17,27,35,56,93,106,117,123,137,148,156,159,181,199,206],"newly":[18],"proposed":[19],"policy":[20,39,47,153,182],"improves":[21],"upon":[22],"a":[23,45,62,70,152],"baseline,":[24],"such":[25],"as":[26],"status":[28],"quo.":[29],"In":[30],"this":[31],"work,":[32],"we":[33],"consider":[34],"problem":[36],"of":[37,73,150,202],"safe":[38,97,203],"improvement,":[40,208],"where":[41],"one":[42],"only":[43],"adopts":[44],"new":[46],"if":[48],"deemed":[51],"be":[53,132],"better":[54],"than":[55,155],"specified":[57],"baseline":[58,107,157],"at":[60],"least":[61],"pre-specified":[63,160],"probability.":[64],"We":[65,111,139,168,186],"focus":[66],"on":[67,86],"threshold":[68],"policies,":[69],"ubiquitous":[71],"class":[72],"applications":[76],"economics,":[78],"healthcare,":[79],"and":[80,91,126,171,191,205,214],"digital":[81],"advertising.":[82],"Existing":[83],"methods":[84],"rely":[85],"potentially":[87],"underpowered":[88],"safety":[89,120,212],"checks":[90],"limit":[92],"opportunities":[94],"for":[95,128,134,176],"finding":[96],"improvements,":[98],"so":[99],"too":[100],"they":[102],"must":[103],"revert":[104],"maintain":[109],"safety.":[110],"overcome":[112],"these":[113],"issues":[114],"by":[115],"leveraging":[116],"most":[118],"powerful":[119],"test":[121],"asymptotic":[124],"regime":[125],"allowing":[127],"multiple":[129],"candidates":[130],"tested":[133],"improvement":[135,183],"over":[136],"baseline.":[138,185],"show":[140],"adversarial":[143],"our":[145,195],"approach":[146],"controls":[147],"rate":[149],"adopting":[151],"worse":[154],"error":[161],"level,":[162],"even":[163],"moderate":[165],"sample":[166],"sizes.":[167],"present":[169],"CSPI":[170],"CSPI-MT,":[172],"two":[173],"novel":[174],"algorithms":[175],"selecting":[177],"cutoff(s)":[178],"maximize":[180],"from":[184],"demonstrate":[187],"through":[188],"both":[189,198],"synthetic":[190],"external":[192],"datasets":[193],"approaches":[196],"improve":[197],"detection":[200],"rates":[201],"realized":[207],"particularly":[209],"under":[210],"stringent":[211],"requirements":[213],"low":[215],"signal-to-noise":[216],"conditions.":[217]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
