Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions

Causal Inference


  • Sara Magliacane
  • Thijs van Ommen
  • Tom Claassen
  • Stephan Bongers
  • Philip Versteeg
  • Joris M. Mooij

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Causal Inference

An important goal common to domain adaptation and causal inference is to make accurate predictions when the distributions for the source (or training) domain(s) and target (or test) domain(s) differ. In many cases, these different distributions can be modeled as different contexts of a single underlying system, in which each distribution corresponds to a different perturbation of the system, or in causal terms, an intervention. We focus on a class of such causal domain adaptation problems, where data for one or more source domains are given, and the task is to predict the distribution of a certain target variable from measurements of other variables in one or more target domains. We propose an approach for solving these problems that exploits causal inference and does not rely on prior knowledge of the causal graph, the type of interventions or the intervention targets. We demonstrate our approach by evaluating a possible implementation on simulated and real world data.

Please cite our work using the BibTeX below.

  author    = {Sara Magliacane and
               Thijs van Ommen and
               Tom Claassen and
               Stephan Bongers and
               Philip Versteeg and
               Joris M. Mooij},
  title     = {Causal Transfer Learning},
  journal   = {CoRR},
  volume    = {abs/1707.06422},
  year      = {2017},
  url       = {},
  archivePrefix = {arXiv},
  eprint    = {1707.06422},
  timestamp = {Mon, 13 Aug 2018 16:47:04 +0200},
  biburl    = {},
  bibsource = {dblp computer science bibliography,}

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