It is common practice to collect observations of feature and response pairs from different environments. A natural question is how to identify features that have consistent prediction power across environments. The invariant causal prediction framework proposes to approach this problem through invariance, assuming a linear model that is invariant under different environments. In this work, we make an attempt to shed light on this framework by connecting it to the Gaussian multiple access channel problem. Specifically, we incorporate optimal code constructions and decoding methods to provide lower bounds on the error probability. We illustrate our findings by various simulation settings.

翻译：收集不同环境的特征和对应对的观测是常见的做法。自然的问题是如何确定具有不同环境一致预测力的特征。无差别的因果预测框架建议通过变化来解决这一问题,假设一种在不同环境中是无差别的线性模型。在这项工作中,我们试图通过将这一框架与高斯多访问频道问题联系起来来阐明这一框架。具体地说,我们纳入了最佳代码构建和解码方法,以提供差错概率的较低界限。我们用各种模拟设置来说明我们的结论。