Deconfounding and Causal Regularization for Stability and External Validity [article]

Peter Bühlmann, Domagoj Ćevid
2020 arXiv   pre-print
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.
arXiv:2008.06234v1 fatcat:ny7axfvicbcczmh24wuaofg2km