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.

我们提出了一种因果学习模型，通过因果推断和干预研究物理现象的因果关系，并加强（或削弱）对潜在物理过程模型的信心。