Dynamic　effective　connectivity　(DEC)　is　the　accumulation　of　effective　connectivity　in　the　time　dimension,　which　can　describe　the　continuous　neural　activities　in　the　brain.　Recently,　learning　DEC　from　functional　magnetic　resonance　imaging　(fMRI)　and　electroencephalography　(EEG)　data　has　attracted　the　attention　of　neuroinformatics　researchers.　However,　the　current　methods　fail　to　consider　the　gap　between　the　fMRI　and　EEG　modality,　which　can　not　precisely　learn　the　DEC　network　from　multimodal　data.　In　this　paper,　we　propose　a　multimodal　causal　adversarial　network　for　DEC　learning,　named　MCAN.　The　MCAN　contains　two　modules:　multimodal　causal　generator　and　multimodal　causal　discriminator.　First,　MCAN　employs　a　multimodal　causal　generator　with　an　attention-guided　layer　to　produce　a　posterior　signal　and　output　a　set　of　DEC　networks.　Then,　the　proposed　method　uses　a　multimodal　causal　discriminator　to　unsupervised　calculate　the　joint　gradient,　which　directs　the　update　of　the　whole　network.　The　experimental　results　on　simulated　data　sets　show　that　MCAN　is　superior　to　other　state-of-the-art　methods　in　learning　the　network　structure　of　DEC　and　can　effectively　estimate　the　brain　states.　The　experimental　results　on　real　data　sets　show　that　MCAN　can　better　reveal　abnormal　patterns　of　brain　activity　and　has　good　application　potential　in　brain　network　analysis.