Invariance, Causality and Robustness
Invariance, Causality and Robustness
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DOI：
10.1214/19-sts721
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发表时间：
2020-08-01
影响因子：
5.7
通讯作者：
Buehlmann, Peter
中科院分区：
文献类型：
--
作者：
Buehlmann, Peter
我们以一种统一的方式讨论了最近关于因果推理和预测稳健性的工作。关键思想依赖于概率不变性或稳定性的概念：它为将因果关系表述为具有相应稳健性概念的特定风险最小化问题开辟了新的见解。这种不变性本身可以从目前数据采集中经常出现的一般非均匀数据或扰动数据中估计出来。这种新的方法在许多应用中具有潜在的实用价值，与标准回归或分类框架中的机器学习或估计相比，它提供了更强的稳健性和更好的“面向因果”的解释。
We discuss recent work for causal inference and predictive robustness in a unifying way. The key idea relies on a notion of probabilistic invariance or stability: it opens up new insights for formulating causality as a certain risk minimization problem with a corresponding notion of robustness. The invariance itself can be estimated from general heterogeneous or perturbation data which frequently occur with nowadays data collection. The novel methodology is potentially useful in many applications, offering more robustness and better "causal-oriented" interpretation than machine learning or estimation in standard regression or classification frameworks.