The advances in Artificial Intelligence (AI) and Machine Learning (ML) have opened up many avenues for scientific research, and are adding new dimensions to the process of knowledge creation. However, even the most powerful and versatile of ML applications till date are primarily in the domain of analysis of associations and boil down to complex data fitting. Judea Pearl has pointed out that Artificial General Intelligence must involve interventions involving the acts of doing and imagining. Any machine assisted scientific discovery thus must include casual analysis and interventions. In this context, we propose a causal learning model of physical principles, which not only recognizes correlations but also brings out casual relationships. We use the principles of causal inference and interventions to study the cause-and-effect relationships in the context of some well-known physical phenomena. We show that this technique can not only figure out associations among data, but is also able to correctly ascertain the cause-and-effect relations amongst the variables, thereby strengthening (or weakening) our confidence in the proposed model of the underlying physical process.

翻译：人工智能（AI）与机器学习（ML）的进步为科学研究开辟了诸多新路径，并为知识创造过程注入了新维度。然而，迄今最强大且通用的机器学习应用仍主要属于关联分析范畴，本质上可归结为复杂数据拟合。Judea Pearl指出，通用人工智能必须包含涉及"行动"与"想象"的干预操作。任何机器辅助的科学发现都必然包含因果分析与干预。在此背景下，我们提出了一种物理原理的因果学习模型，该模型不仅能识别相关性，还能揭示因果关系。我们通过因果推断与干预原理，研究若干著名物理现象中的因果关系。研究表明，该技术不仅能发现数据间的关联，还能准确判定变量间的因果效应，从而增强（或削弱）我们对所提出的物理过程模型的置信度。