论文标题
不变因果预测的误差概率的下限
Lower Bounds on the Error Probability for Invariant Causal Prediction
论文作者
论文摘要
从不同环境中收集特征和响应对的观察是普遍的做法。一个自然的问题是如何识别在环境中具有一致预测能力的功能。不变的因果预测框架提议通过不变性解决此问题，假设在不同环境下是不变的线性模型。在这项工作中，我们试图通过将其连接到高斯多访问通道问题来阐明该框架。具体而言，我们合并了最佳代码构造和解码方法，以提供误差概率的下限。我们通过各种模拟设置说明了我们的发现。
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.
