  Without loss of generality, existing machine learning techniques may learn
spurious correlation dependent on the domain, which exacerbates the
generalization of models in out-of-distribution (OOD) scenarios. To address
this issue, recent works build a structural causal model (SCM) to describe the
causality within data generation process, thereby motivating methods to avoid
the learning of spurious correlation by models. However, from the machine
learning viewpoint, such a theoretical analysis omits the nuanced difference
between the data generation process and representation learning process,
resulting in that the causal analysis based on the former cannot well adapt to
the latter. To this end, we explore to build a SCM for representation learning
process and further conduct a thorough analysis of the mechanisms underlying
spurious correlation. We underscore that adjusting erroneous covariates
introduces bias, thus necessitating the correct selection of spurious
correlation mechanisms based on practical application scenarios. In this
regard, we substantiate the correctness of the proposed SCM and further propose
to control confounding bias in OOD generalization by introducing a propensity
score weighted estimator, which can be integrated into any existing OOD method
as a plug-and-play module. The empirical results comprehensively demonstrate
the effectiveness of our method on synthetic and large-scale real OOD datasets.
