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Invariant causal prediction (ICP, Peters et al. (2016)) provides a novel wayto identify causal predictors of a response by utilizing heterogeneous datafrom different environments. One advantage of ICP is that it guarantees to makeno false causal discoveries with high probability. Such a guarantee, however,can be too conservative in some applications, resulting in few or nodiscoveries. To address this, we propose simultaneous false discovery boundsfor ICP, which provides users with extra flexibility in exploring causalpredictors and can extract more informative results. These additionalinferences come for free, in the sense that they do not require additionalassumptions, and the same information obtained by the original ICP is retained.We demonstrate the practical usage of our method through simulations and a realdataset.

Simultaneous false discovery bounds for invariant causal prediction