  Learning models that offer robust out-of-distribution generalization and fast
adaptation is a key challenge in modern machine learning. Modelling causal
structure into neural networks holds the promise to accomplish robust zero and
few-shot adaptation. Recent advances in differentiable causal discovery have
proposed to factorize the data generating process into a set of modules, i.e.
one module for the conditional distribution of every variable where only causal
parents are used as predictors. Such a modular decomposition of knowledge
enables adaptation to distributions shifts by only updating a subset of
parameters. In this work, we systematically study the generalization and
adaption performance of such modular neural causal models by comparing it to
monolithic models and structured models where the set of predictors is not
constrained to causal parents. Our analysis shows that the modular neural
causal models outperform other models on both zero and few-shot adaptation in
low data regimes and offer robust generalization. We also found that the
effects are more significant for sparser graphs as compared to denser graphs.
