As　a　continual　learning　paradigm　where　non-stationary　data　arrive　in　the　form　of　streams　and　training　occurs　whenever　a　small　batch　of　samples　is　accumulated,　general　continual　learning　(GCL)　suffers　from　both　inter-task　bias　and　intra-task　bias.　Existing　GCL　methods　can　hardly　simultaneously　handle　two　issues　since　it　requires　models　to　avoid　from　lying　into　the　spurious　correlation　trap　of　GCL.　From　a　causal　perspective,　we　formalize　a　structural　causality　model　of　GCL　and　conclude　that　spurious　correlation　exists　not　only　between　confounders　and　input,　but　also　within　multiple　causal　variables.　Inspired　by　frequency　transformation　techniques　which　harbor　intricate　patterns　of　image　comprehension,　we　propose　a　plug-and-play　module:　the　Dual-Domain　Division　Multiplex　(D3M)　unit,　which　intervenes　confounders　and　multiple　causal　factors　over　frequency　and　spatial　domains　with　a　two-stage　pseudo　causal　intervention　strategy.　Typically,　D3M　consists　of　a　frequency　division　multiplexer　(FDM)　module　and　a　spatial　division　multiplexer　(SDM)　module,　each　of　which　prioritizes　target-relevant　causal　features　by　dividing　and　multiplexing　features　over　frequency　domain　and　spatial　domain,　respectively.　As　a　lightweight　and　model-agonistic　unit,　D3M　can　be　seamlessly　integrated　into　most　current　GCL　methods.　Extensive　experiments　on　four　popular　datasets　demonstrate　that　D3M　significantly　enhances　accuracy　and　diminishes　catastrophic　forgetting　compared　to　current　methods.　The　code　is　available　at　https://github.com/wangsfan/D3M.