Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis
稀疏机制变化假说下异质环境中的因果发现
Machine learning approaches commonly rely on the assumption of independent and identically distributed (i.i.d.) data. In reality, however, this assumption is almost always violated due to distribution shifts between environments. Although valuable learning signals can be provided by heterogeneous data from changing distributions, it is also known that learning under arbitrary (adversarial) changes is impossible. Causality provides a useful framework for modeling distribution…