title: Learning Independent Causal Mechanisms
abstract: Independent causal mechanisms are a central concept in the study of causality
  with implications for machine learning tasks. In this work we develop
  an algorithm to recover a set of (inverse) independent mechanisms relating
  a distribution transformed by the mechanisms to a reference distribution.
  The approach is fully unsupervised and based on a set of experts that compete
  for data to specialize and extract the mechanisms. We test and analyze
  the proposed method on a series of experiments based on image transformations.
  Each expert successfully maps a subset of the transformed data
  to the original domain, and the learned mechanisms generalize to other
  domains. We discuss implications for domain transfer and links to recent
  trends in generative modeling.
authors: Giambattista Parascandolo Mateo Rojas Carulla Niki Kilbertus Bernhard Schoelkopf
hIndex: Giambattista Parascandolo 14