Invariance, Causality and Robustness

• Bühlmann, Peter ^[1]
  1. [1] Swiss Federal Institute of Technology in Zurich

     Swiss Federal Institute of Technology in Zurich

     Zürich, Suiza

     
• Localización: Statistical science, ISSN 0883-4237, Vol. 35, Nº. 3, 2020 (Ejemplar dedicado a: Causal Inference), págs. 404-426
• Idioma: inglés
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• Resumen

  • 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.