Article,
Invariance, Causality and Robustness
(2018)cite arxiv:1812.08233Comment: 36 pages, 12 figures.
Abstract
We discuss recent work for causal inference and predictive robustness in a
unifying way. The key idea relies on a notion of probabilistic invariance or
stability: it opens up new insights for formulating causality as a certain risk
minimization problem with a corresponding notion of robustness. The invariance
itself can be estimated from general heterogeneous or perturbation data which
frequently occur with nowadays data collection. The novel methodology is
potentially useful in many applications, offering more robustness and better
`causal-oriented' interpretation than machine learning or estimation in
standard regression or classification frameworks.
