  Causal inference has become a powerful tool to handle the out-of-distribution
(OOD) generalization problem, which aims to extract the invariant features.
However, conventional methods apply causal learners from multiple data splits,
which may incur biased representation learning from imbalanced data
distributions and difficulty in invariant feature learning from heterogeneous
sources. To address these issues, this paper presents a balanced meta-causal
learner (BMCL), which includes a balanced task generation module (BTG) and a
meta-causal feature learning module (MCFL). Specifically, the BTG module learns
to generate balanced subsets by a self-learned partitioning algorithm with
constraints on the proportions of sample classes and contexts. The MCFL module
trains a meta-learner adapted to different distributions. Experiments conducted
on NICO++ dataset verified that BMCL effectively identifies the class-invariant
visual regions for classification and may serve as a general framework to
improve the performance of the state-of-the-art methods.
