Causal inference using invariant prediction: identification and confidence intervals

@article{Peters2015CausalIU,
  title={Causal inference using invariant prediction: identification and confidence intervals},
  author={J. Peters and Peter Buhlmann and N. Meinshausen},
  journal={arXiv: Methodology},
  year={2015}
}
  • J. Peters, Peter Buhlmann, N. Meinshausen
  • Published 2015
  • Mathematics, Computer Science
  • arXiv: Methodology
  • What is the difference of a prediction that is made with a causal model and a non-causal model. [...] Key Method Here, we propose to exploit this invariance of a prediction under a causal model for causal inference: given different experimental settings (for example various interventions) we collect all models that do show invariance in their predictive accuracy across settings and interventions. The causal model will be a member of this set of models with high probability. This approach yields valid confidence…Expand Abstract
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