Identifying Cause and Effect on Discrete Data using Additive Noise Models

  title={Identifying Cause and Effect on Discrete Data using Additive Noise Models},
  author={Jonas Peters and Dominik Janzing and Bernhard Sch{\"o}lkopf},
Inferring the causal structure of a set of random variables from a finite sample of the joint distribution is an important problem in science. Recently, methods using additive noise models have been suggested to approach the case of continuous variables. In many situations, however, the variables of interest are discrete or even have only finitely many states. In this work we extend the notion of additive noise models to these cases. Whenever the joint distribution P ) admits such a model in… CONTINUE READING
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