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.

翻译：为何现象会发生？解答这一问题构成基于经验观测的大多数科学探究的核心，且往往高度依赖于科学模型的仿真。随着模型日益复杂，在相互关联变量的高维空间中解读这些现象背后的因果机制变得越来越具有挑战性。因果机器学习可辅助科学家在仿真中发现相关且可解释的因果模式。我们提出"目标因果简化"（TCR）方法，旨在将复杂模型转化为解释特定目标现象的简洁因果因子集。我们推导出信息论目标函数以从干预数据或仿真中学习TCR，并提出了高效优化该目标的算法。通过在玩具系统与机械系统中的验证，TCR展现了从复杂模型中生成可解释高层解释的能力，彰显了其在多学科领域辅助科学家研究复杂现象的潜力。