  Recently, molecular relational learning, whose goal is to predict the
interaction behavior between molecular pairs, got a surge of interest in
molecular sciences due to its wide range of applications. In this work, we
propose CMRL that is robust to the distributional shift in molecular relational
learning by detecting the core substructure that is causally related to
chemical reactions. To do so, we first assume a causal relationship based on
the domain knowledge of molecular sciences and construct a structural causal
model (SCM) that reveals the relationship between variables. Based on the SCM,
we introduce a novel conditional intervention framework whose intervention is
conditioned on the paired molecule. With the conditional intervention
framework, our model successfully learns from the causal substructure and
alleviates the confounding effect of shortcut substructures that are spuriously
correlated to chemical reactions. Extensive experiments on various tasks with
real-world and synthetic datasets demonstrate the superiority of CMRL over
state-of-the-art baseline models. Our code is available at
https://github.com/Namkyeong/CMRL.
