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.

翻译：不变因果预测（ICP，Peters et al. (2016)）通过利用来自不同环境下的异质性数据，提供了一种识别响应变量因果预测因子的新颖方法。ICP的一个优势在于能以高概率保证不产生虚假的因果发现。然而，这种保证在某些应用中可能过于保守，导致发现结果稀少甚至没有发现。为解决此问题，我们为ICP提出了同时错误发现界，这为用户探索因果预测因子提供了额外的灵活性，并能提取更具信息量的结果。这些附加推断无需额外假设即可免费获得，且原始ICP所获取的信息得以保留。我们通过模拟实验和一个真实数据集验证了该方法在实际应用中的效果。