With　ubiquitous　adoption　of　machine　learning　algorithms　in　web　technologies,　such　as　recommendation　system　and　social　network,　algorithm　fairness　has　become　a　trending　topic,　and　it　has　a　great　impact　on　social　welfare.　Among　different　fairness　definitions,　path-specific　causal　fairness　is　a　widely　adopted　one　with　great　potentials,　as　it　distinguishes　the　fair　and　unfair　effects　that　the　sensitive　attributes　exert　on　algorithm　predictions.　Existing　methods　based　on　path-specific　causal　fairness　either　require　graph　structure　as　the　prior　knowledge　or　have　high　complexity　in　the　calculation　of　path-specific　effect.　To　tackle　these　challenges,　we　propose　a　novel　casual　graph　based　fair　prediction　framework　which　integrates　graph　structure　learning　into　fair　prediction　to　ensure　that　unfair　pathways　are　excluded　in　the　causal　graph.　Furthermore,　we　generalize　the　proposed　framework　to　the　scenarios　where　sensitive　attributes　can　be　non-root　nodes　and　affected　by　other　variables,　which　is　commonly　observed　in　real-world　applications,　such　as　recommendation　system,　but　hardly　addressed　by　existing　works.　We　provide　theoretical　analysis　on　the　generalization　bound　for　the　proposed　fair　prediction　method,　and　conduct　a　series　of　experiments　on　real-world　datasets　to　demonstrate　that　the　proposed　framework　can　provide　better　prediction　performance　and　algorithm　fairness　trade-off.　©　2023　ACM.