Inferring　effective　connectivity　between　different　brain　regions　from　functional　magnetic　resonance　imaging　(fMRI)　data　is　an　important　advanced　study　in　neuroinformatics　in　recent　years.　However,　current　methods　have　limited　usage　in　effective　connectivity　studies　due　to　the　high　noise　and　small　sample　size　of　fMRI　data.　In　this　paper.　we　propose　a　novel　framework　for　inferring　effective　connectivity　based　on　generative　adversarial　networks　(GAN),　named　as　EC-GAN.　The　proposed　framework　EC-GAN　infers　effective　connectivity　via　an　adversarial　process,　in　which　we　simultaneously　train　two　models:　a　generator　and　a　discriminator.　The　generator　consists　of　a　set　of　effective　connectivity　generators　based　on　structural　equation　models　which　can　generate　the　fMRI　time　series　of　each　brain　region　via　effective　connectivity.　Meanwhile,　the　discriminator　is　employed　to　distinguish　between　the　joint　distributions　of　the　real　and　generated　fMRI　time　series.　Experimental　results　on　simulated　data　show　that　EC-GAN　can　better　infer　effective　connectivity　compared　to　other　state-of-the-art　methods.　The　real-world　experiments　indicate　that　EC-GAN　can　provide　a　new　and　reliable　perspective　analyzing　the　effective　connectivity　of　fMRI　data.