Using　machine　learning　methods　to　estimate　brain　effective　connectivity　networks　from　functional　magnetic　resonance　imaging　(fMRI)　data　has　garnered　significant　attention　in　the　fields　of　neuroinformatics　and　bioinformatics.　However,　existing　methods　usually　require　retraining　the　model　for　each　subject,　which　ignores　the　knowledge　shared　across　subjects.　In　this　paper,　we　propose　a　novel　framework　for　estimating　effective　connectivity　based　on　an　amortization　transformer,　named　AT-EC.　In　detail,　AT-EC　first　employs　an　amortization　transformer　to　model　the　dynamics　of　fMRI　time　series　and　infer　brain　effective　connectivity　across　different　subjects,　which　can　train　an　amortized　model　that　leverages　the　shared　knowledge　from　different　subjects.　Then,　an　assisted　learning　mechanism　based　on　functional　connectivity　is　designed　to　assist　the　estimation　of　the　brain　effective　connectivity　network.　Experimental　results　on　both　simulated　and　real-world　data　demonstrate　the　efficacy　of　our　method.