Why does a phenomenon occur? Addressing this question is central to most
scientific inquiries based on empirical observations, and often heavily relies
on simulations of scientific models. As models become more intricate,
deciphering the causes behind these phenomena in high-dimensional spaces of
interconnected variables becomes increasingly challenging. Causal machine
learning may assist scientists in the discovery of relevant and interpretable
patterns of causation in simulations. We introduce Targeted Causal Reduction
(TCR), a method for turning complex models into a concise set of causal factors
that explain a specific target phenomenon. We derive an information theoretic
objective to learn TCR from interventional data or simulations and propose
algorithms to optimize this objective efficiently. TCR's ability to generate
interpretable high-level explanations from complex models is demonstrated on
toy and mechanical systems, illustrating its potential to assist scientists in
the study of complex phenomena in a broad range of disciplines.

为了解决复杂模型中高维空间的因果关系模式识别的问题，我们引入了目标因果约简（Targeted Causal Reduction，TCR）方法来将复杂模型转化为一组简洁的因果因素，并通过玩具和机械系统的实例验证了 TCR 从复杂模型中生成可解释的高层解释的能力，展示了它在广泛学科中辅助科学家研究复杂现象的潜力。

有针对性地减少因果模型

Targeted Reduction of Causal Models

Emergence is a pregnant property in various fields. It is the fact for a
phenomenon to appear surprisingly and to be such that it seems at first sight
that it is not possible to predict its apparition. That is the reason why it
has often been said that emergence is a subjective property relative to the
observer. Some mathematical systems having very simple and deterministic rules
nevertheless show emergent behavior. Studying these systems shed a new light on
the subject and allows to define a new concept, computational irreducibility,
which deals with behaviors that even though they are totally deterministic
cannot be predicted without simulating them. Computational irreducibility is
then a key for understanding emergent phenomena from an objective point of view
that does not need the mention of any observer.

出现是各个领域中的一个重要属性，可以突如其来地出现，初看并不能预测其出现。因此被认为是相对于观察者的主观属性。一些具有简单和确定性规则的数学系统却展现出突出的行为。研究这些系统为对主观和客观不需要观察者的理解提供了新思路，使得描述计算不可简化成为理解出现现象的关键。