Estimating　effective　connectivity　from　functional　magnetic　resonance　imaging　(fMRI)　time　series　data　has　become　a　very　hot　topic　in　neuroinformatics　and　brain　informatics.　However,　it　is　hard　for　the　current　methods　to　accurately　estimate　the　effective　connectivity　due　to　the　high　noise　and　small　sample　size　of　fMRI　data.　In　this　paper,　we　propose　a　novel　framework　for　estimating　effective　connectivity　based　on　recurrent　generative　adversarial　networks,　called　EC-RGAN.　The　proposed　framework　employs　the　generator　that　consists　of　a　set　of　effective　connectivity　generators　based　on　recurrent　neural　networks　to　generate　the　fMRI　time　series　of　each　brain　region,　and　uses　the　discriminator　to　distinguish　between　the　joint　distributions　of　the　real　and　generated　fMRI　time　series.　When　the　model　is　well-trained　and　generated　fMRI　data　is　similar　to　real　fMRI　data,　EC-RGAN　outputs　the　effective　connectivity　by　means　of　the　causal　parameters　of　the　effective　connectivity　generators.　Experimental　results　on　both　simulated　and　real-world　fMRI　time　series　data　demonstrate　the　efficacy　of　our　proposed　framework.