在大规模非线性时间序列数据集中检测并量化因果关联

Detectingandquantifyingcausalassociationsinlargenonlineartimeseriesdatasets

在大规模非线性时间序列数据集中检测并量化因果关联作者介绍本文目前被引12次（谷歌学术）目录IntroductionGraphmodelCausalnetworkdiscoverywithPCMCIResultsConclusionsandDiscusionsIntroductionIntroductionWalkercirculationWarmsurfaceairtemperatureanomaliesintheEastPacific

(EPAC)arecarriedwestwardbytradewindsacrosstheCentralPacific

(CPAC).ThemoistairrisesovertheWestPacific

(WPAC),andthecirculationisclosedbythecoolanddryairsinkingeastwardacrosstheentiretropicalPacific.Furthermore,theCPACregionlinkstemperatureanomaliestothetropicalAtlantic(ATL)viaanatmosphericbridge.IntroductionJ.Runge,S.Bathiany,E.Bollt,G.etc,Inferringcausationfromtimeseriesinearthsystemsciences.Nat.Commun.10,2553(2019).IntroductionTwomainfactorsthatleadthecommonautoregressive

Grangercausalmodelingapproachtohavelowdetectionpower:reducedeffectsizeduetoconditioningonirrelevantvariables;highdimensionality.Introductiondetectionpower

dependson:theavailablesamplesize,thesignificancelevel,thedimensionalityoftheproblem(e.g.,thenumberofcoefficientsinanautoregressivemodel),andtheeffectsize,isthemagnitudeoftheeffectasmeasuredbytheteststatistic(e.g.,thepartialcorrelationcoefficient).IntroductionNino:

tropicalPacificsurfacetemperatures,monthlyNino3.4indexBCT:extratropicallandairtemperaturesoverBritishColumbia(BCT)for1979–2017Nino→BCTIntroductionAlgorithm:Corr:time-laggedcorrelationanalysis;FullCI:conditionalindependence(Grangercausality);PCMCI:PC+MCI(momentaryconditionalindependence).Signals:fori=2,4,6fori=1,3,5IntroductionIntroductiondilemma:Tostrengthenthecredibilityofcausalinterpretations,weneedtoincludemorevariablesthatmightexplainaspuriousrelationship,buttheseleadtolowerpowertodetecttruecausallinksduetohigherdimensionalityandpossiblylowereffectsize.Graphmodelnodeaistheparentofnodeb,nodebisthechildofnodea.fullyconnectedGraphmodelNO

directedcyclesDAG:directedacyclicgraphs有向无环图Graphmodelconditionalindependenceaisconditionallyindependentofbgivenctail-to-tailhead-to-tailhead-to-headCausalnetworkdiscoverywithPCMCITimeseriesgraphscausalparentsedgesnodesorabsenceofconditionalindependenceCausalnetworkdiscoverywithPCMCITwostepsofPCMCI:PCconditionselectiontoidentifyrelevantconditions(causalparents)foralltimeseriesvariablesXjt;themomentaryconditionalindependence(MCI)testtotestwhetherBoththePC1andtheMCIstagecanbeflexiblycombinedwithanykindofconditionalindependencetest:linearpartialcorrelation(ParCorr)GPDC(Gaussianprocessregressionandadistancecorrelationtestontheresiduals)CMI(ak-nearestneighborestimatorofconditionalmutualinformation)CausalnetworkdiscoverywithPCMCIincontrasttoconditioningonthewholepastofallprocessesasinFullCI,conditioningonlyonasetthatatleastincludestheparentsofavariableXjtsufficestoidentifyspuriouslinks.MarkovdiscoveryalgorithmsC.F.Aliferis,A.Statnikov,I.Tsamardinos,S.Mani,X.Koutsoukos,LocalcausalandmarkovblanketinductionforcausaldiscoveryandfeatureselectionforclassificationpartI:Algorithmsandempiricalevaluation.

J.Mach.Learn.Res.11,171–234(2010).PCalgorithmsP.Spirtes,C.Glymour,Analgorithmforfastrecoveryofsparsecausalgraphs.Soc.Sci.Comput.Rev.9,62–72(1991).CausalnetworkdiscoverywithPCMCICausalnetworkdiscoverywithPCMCICausalnetworkdiscoverywithPCMCIaconditionselectionstagetoremoveirrelevantvariablesaconditionalindependencetestdesignedforhighlyinterdependenttimeseriesimprovespowerlevelsforlarge-scalecausaldiscoveryanalysesyieldsmorepowerthanclassicaltechniquesinanalysesinvolvingonlyfewvariablescontrolledfalse-positiveratesPCMCIResultsZisnotaparentofBCT;hasthesamepartialcorrelationeffectsize≈0.10;detectionpower>80%evenforthehigh-dimensionalcase.Results:WalkercirculationEastPacific(EPAC),CentralPacific(CPAC),WestPacific(WPAC),andtropicalAtlantic(ATL)heartrate(B),diastolic(D),andsystolic(S)bloodpressureofpregnanthealthywomenResults:datasetResults:linearcausaldiscoverymethodsResultsadvantageouspropertiesofPCMCIcomputationalcomplexity,consistency,largereffectsize;normalizedcausalstrengthtorankcausallinks.ConclusionsandDiscussionsPCMCIhassignificantlyhigherdetectionpowerthanestablishedmethodssuchasLasso,thePCalgorithm,orGrangercausalityanditsnonlinearextensionsfortimeseriesdatasetsontheorderofdozenstohundredsofvariables.TheMCIteststatisticitselfcanbeinterpretedasameasureofcausalstrength,allowingustodirectlyrankcausallinks.Con