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摘要
En 中文
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.
Keyword:
Anchor regression
causal regularization
distributional robustness
heterogeneous data
instrumental variables regression
interventional data
Random Forests
variable importance
期刊
IF:
3.4
论文数:
1.0K
被引数:
8.7K
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