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方法。ICP的一个优势是能以较高概率保证不产生虚假因果发现。然而，这种保证在某些应用中可能过于保守，导致发现结果极少甚至没有发现。为解决这一问题，我们提出了ICP的同时误发现界，这为用户探索因果预测因子提供了额外灵活性，并能提取更具信息量的结果。这些额外推断无需额外假设，且原始ICP所获得的信息得以保留，即它们可“免费”获得。我们通过模拟实验和一个真实数据集展示了所提出方法的实际应用效果。