高维数据下的几类因果推断算法研究的开题报告.docx

高维数据下的几类因果推断算法研究的开题报告
Abstract
Causalinferenceisimportantinmanyareassuchassocialscienceandmedicine,anditseekstoidentifythecausalrelationshipsbetweendifferentvariables.Inrecentyears,withtherapiddevelopmentofdatascienceandmachinelearningtechniques,manycausalinferencealgorithmshavebeenproposed.However,thesealgorithmsmaynotbeeffectivewhendealingwithhigh-dimensionaldata,whichoftencontainsmanyirrelevantandnoisyfeatures.Inthisproject,wewillreviewseveralstate-of-the-artcausalinferencealgorithmsforhigh-dimensionaldata,andinvestigatetheirstrengthsandweaknesses.Wewillalsoproposenewalgorithmsorimprovementstoexistingalgorithmstoaddressthechallengesinhigh-dimensionaldata.
Introduction
Causalinferenceistheprocessofdeterminingthecausalrelationshipsbetweendifferentvariables.Forexample,inmedicalresearch,wemaybeinterestedindeterminingwhetheracertainmedicationcausesareductioninbloodpressure.Insocialscience,wemaywanttostudytheeffectofeducationonincome.Causalinferenceisimportantbecauseitallowsustomakeinformeddecisionsandtakenecessaryactionsbasedonthecausalrelationshipsbetweenvariables.
Inrecentyears,manycausalinferencealgorithmshavebeenproposed,suchaspropensityscorematching,instrumentalvariableanalysis,andregressiondiscontinuitydesign.However,thesealgorithmsmaynotbeeffectivewhendealingwithhigh-dimensionaldata,whichoftencontainsmanyirrelevantandnoisyfeatures.High-dimensionaldatareferstodatasetswithalargenumberoffeaturesrelativetothenumberofobservations.Insuchdatasets,traditionalstatisticalmethodssuchaslinearregressionmaysufferfromthecurseofdimensionality,whichleadstooverfittingandpoorgeneralizationperformance.Therefore,newalgorithmsorimprovementstoexistingalgorithmsareneededtoaddressthechallengesinhigh-dimensionaldata.
Objectives
Theobjectivesofthisprojectare:
1.Toreviewseveralstate-of-the-artcausalinferencealgorithmsforhigh-dimensionaldata,includingbutnotlimitedtothefollowing:
*High-DimensionalPropensityScore(HDPS)Matching
*High-DimensionalInstrumentalVariable(HDIV)Analysis
*SparseRegres