  We review some recent work on removing hidden confounding and causal
regularization from a unified viewpoint. We describe how simple and
user-friendly techniques improve stability, replicability and distributional
robustness in heterogeneous data. In this sense, we provide additional thoughts
to the issue on concept drift, raised by Efron (2020), when the data generating
distribution is changing.
