Effective connectivity analysis for noninvasive recordings such as electroencephalograms (EEGs) is drawing more attention in the whole brain-level studies. Most of previous studies have aimed to extract the causal relations between brain regions during the resting states or cognitive tasks, but it is not clear that the derived connectivity is valid. In general, it is difficult to identify causality in such a usual experimental framework based on observations alone because it is necessary to compare effects between the conditions with and without the cause. Transcranial magnetic stimulation (TMS) provides a framework in which a controllable perturbation is applied to a local brain region and the effect is examined by comparing the neural activity with and without this stimulation. The present study applies two representative methods for effective connectivity analysis, symbolic transfer entropy (STE) and vector autoregression (VAR), to TMS-EEG data to see if the methods can correctly extract the causal relations given by TMS. In terms of the consistency of results from different experimental sessions, STE is found to yield robust results irrespective of sessions, whereas the results by VAR showed less correlation between sessions. Furthermore, STE preferentially detects the directional information flow from the TMS target. Taken together, our results suggest that STE is a reliable method for detecting the effect of TMS, implying that it would also be useful for identifying neural activity during cognitive tasks and resting states.