Recently, methods have been proposed that exploit the invariance of prediction models with respect to changing environments to infer subsets of the causal parents of a response variable. If the environments influence only few of the underlying mechanisms, the subset identified by invariant causal prediction (ICP), for example, may be small, or even empty. We introduce the concept of minimal invariance and propose invariant ancestry search (IAS). In its population version, IAS outputs a set which contains only ancestors of the response and is a superset of the output of ICP. When applied to data, corresponding guarantees hold asymptotically if the underlying test for invariance has asymptotic level and power. We develop scalable algorithms and perform experiments on simulated and real data.
翻译:最近,有人提议一些方法,利用在变化环境中预测模型的变化,推断反应变量的因果母子子集;如果环境只影响少数基本机制,例如,因果预测(IPC)所查明的子集可能很小,甚至是空的;我们引入了最低变数概念,并提出无变数祖先搜索(IAS)。在人口版中,IAS产生一套只包含反应的先辈的系统,是比较方案产出的超级集。在数据应用时,如果对不变数的基本测试具有无常数水平和力量,相应的保障将无常数地维持。我们开发了可缩放的算法,并进行了模拟和真实数据的实验。