The ability of an agent to perform well in new and unseen environments is a
crucial aspect of intelligence. In machine learning, this ability is referred
to as strong or out-of-distribution generalization. However, simply considering
differences in data distributions is not sufficient to fully capture
differences in environments. In the present paper, we assay out-of-variable
generalization, which refers to an agent's ability to handle new situations
that involve variables never jointly observed before. We expect that such
ability is important also for AI-driven scientific discovery: humans, too,
explore 'Nature' by probing, observing and measuring subsets of variables at
one time. Mathematically, it requires efficient re-use of past marginal
knowledge, i.e., knowledge over subsets of variables. We study this problem,
focusing on prediction tasks that involve observing overlapping, yet distinct,
sets of causal parents. We show that the residual distribution of one
environment encodes the partial derivative of the true generating function with
respect to the unobserved causal parent. Hence, learning from the residual
allows zero-shot prediction even when we never observe the outcome variable in
the other environment.

本文旨在研究 AI 智能代理器对于陌生和新的因变量的预测和探索问题，提出了一种基于先前经验的较为有效的处理方法，并能够实现零样本预测。