Thisbookprovidesadeepunderstandingoftherelationshipbetweenmachinelearningandcausalinference.Itcoversabroadrangeoftopics,startingwiththepreliminaryfoundationsofcausalinference,whichincludebasicdefinitions,illustrativeexamples,andassumptions.Itthendelvesintothedifferenttypesofclassicalcausalinferencemethods,suchasmatching,weighting,tree-basedmodels,andmore.Additionally,thebookexploreshowmachinelearningcanbeusedforcausaleffectestimationbasedonrepresentationlearningandgraphlearning.Thecontributionofcausalinferenceincreatingtrustworthymachinelearningsystemstoaccomplishdiversity,non-discriminationandfairness,transparencyandexplainability,generalizationandrobustness,andmoreisalsodiscussed.Thebookalsoprovidespracticalapplicationsofcausalinferenceinvariousdomainssuchasnaturallanguageprocessing,recommendersystems,computervision,timeseriesforecasting,andcontinuallearning.Eachchapterofthebookiswrittenbyleadingresearchersintheirrespectivefields.MachineLearningforCausalInferenceexploresthechallengesassociatedwiththerelationshipbetweenmachinelearningandcausalinference,suchasbiasedestimatesofcausaleffects,untrustworthymodels,andcomplicatedapplicationsinotherartificialintelligencedomains.However,italsopresentspotentialsolutionstotheseissues.Thebookisavaluableresourceforresearchers,teachers,practitioners,andstudentsinterestedinthesefields.Itprovidesinsightsintohowcombiningmachinelearningandcausalinferencecanimprovethesystem’scapabilitytoaccomplishcausalartificialintelligencebasedondata.Thebookshowcasespromisingresearchdirectionsandemphasizestheimportanceofunderstandingthecausalrelationshiptoconstructdifferentmachine-learningmodelsfromdata.
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