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

Inferring physical laws by artificial intelligence based causal models