Invariance, Causality and Robustness

doi:10.1214/19-STS721

Invariance, Causality and Robustness

2020-08-01

74

OA

AI

P

Peter Bühlmann *

DOI：10.1214/19-STS721

Original

Original request for help

Save

Abstract

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.

Keywords:

Anchor regression

causal regularization

distributional robustness

heterogeneous data

instrumental variables regression

interventional data

Random Forests

variable importance

Invariance, Causality and Robustness

Abstract

Journal

Organization