The abilities of humans to understand the world in terms of cause and effect
relationships, as well as to compress information into abstract concepts, are
two hallmark features of human intelligence. These two topics have been studied
in tandem in the literature under the rubric of causal abstractions theory. In
practice, it remains an open problem how to best leverage abstraction theory in
real-world causal inference tasks, where the true mechanisms are unknown and
only limited data is available. In this paper, we develop a new family of
causal abstractions by clustering variables and their domains. This approach
refines and generalizes previous notions of abstractions to better accommodate
individual causal distributions that are spawned by Pearl's causal hierarchy.
We show that such abstractions are learnable in practical settings through
Neural Causal Models (Xia et al., 2021), enabling the use of the deep learning
toolkit to solve various challenging causal inference tasks -- identification,
estimation, sampling -- at different levels of granularity. Finally, we
integrate these results with representation learning to create more flexible
abstractions, moving these results closer to practical applications. Our
experiments support the theory and illustrate how to scale causal inferences to
high-dimensional settings involving image data.

通过聚类变量及其域的方法，发展了一种新的因果抽象方法，以更好地适应由 Pearl 的因果层次结构引发的个体因果分布，并进一步将这些结果与表示学习相结合，将这些结果更接近实际应用。

神经因果抽象

Neural Causal Abstractions

Structural causal models provide a formalism to express causal relations
between variables of interest. Models and variables can represent a system at
different levels of abstraction, whereby relations may be coarsened and refined
according to the need of a modeller. However, switching between different
levels of abstraction requires evaluating a trade-off between the consistency
and the information loss among different models. In this paper we introduce a
family of interventional measures that an agent may use to evaluate such a
trade-off. We consider four measures suited for different tasks, analyze their
properties, and propose algorithms to evaluate and learn causal abstractions.
Finally, we illustrate the flexibility of our setup by empirically showing how
different measures and algorithmic choices may lead to different abstractions.

本文介绍了一系列干预措施，以帮助代理人在不同抽象水平之间进行权衡，探究其中信息的一致性和损失，并提出了用于评估和学习因果抽象的算法。最后，我们通过实证表明了不同措施和算法选择可能导致不同的抽象。