  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.
