Using　machine　learning　methods　to　estimate　brain　effective　connectivity　networks　from　functional　magnetic　resonance　imaging　(fMRI)　data　has　gradually　become　one　of　the　hot　subjects　in　the　fields　of　neuroscience.　In　particular,　the　encoder–decoder　based　methods　can　effectively　extract　the　connections　in　fMRI　time　series,　which　have　achieved　promising　performance.　However,　these　methods　generally　use　Granger　causality　model,　which　may　identify　false　directions　due　to　the　non-stationary　characteristic　of　fMRI　data.　Additionally,　fMRI　datasets　have　limited　sample　sizes,　which　significantly　constrains　the　development　of　these　methods.　In　this　paper,　we　propose　a　novel　brain　effective　connectivity　estimation　method　based　on　causal　autoencoder　with　meta-knowledge　transfer,　called　MetaCAE.　The　proposed　approach　employs　a　causal　autoencoder　(CAE)　to　extract　causal　dependencies　from　non-stationary　fMRI　time　series,　and　leverages　meta-knowledge　transfer　to　improve　the　estimation　accuracy　on　small-sample　data.　More　specifically,　MetaCAE　first　employs　a　temporal　convolutional　encoder　to　extract　non-stationary　temporal　information　from　fMRI　time　series.　Then　it　uses　a　structural　equation　model-based　decoder　to　decode　causal　relationships　between　brain　regions.　Finally,　it　utilizes　a　model-agnostic　meta-learning　method　to　learn　the　meta-knowledge　of　the　shared　brain　effective　connectivity　among　different　subjects,　and　transfers　the　meta-knowledge　to　the　CAE　to　enhance　its　estimation　ability　on　small-sample　fMRI　data.　Comprehensive　experiments　on　both　simulated　and　real-world　data　demonstrate　the　efficacy　of　MetaCAE　in　estimating　brain　effective　connectivity.　©　2024　Elsevier　Ltd