Invariant causal prediction (ICP, Peters et al. (2016)) provides a novel way to identify causal predictors of a response by utilizing heterogeneous data from different environments. One advantage of ICP is that it guarantees to make no false causal discoveries with high probability. Such a guarantee, however, can be too conservative in some applications, resulting in few or no discoveries. To address this, we propose simultaneous false discovery bounds for ICP, which provides users with extra flexibility in exploring causal predictors and can extract more informative results. These additional inferences come for free, in the sense that they do not require additional assumptions, and the same information obtained by the original ICP is retained. We demonstrate the practical usage of our method through simulations and a real dataset.

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