Graphical identifiability criteria for causal effects in studies with an unobserved treatment / response variable

@inproceedings{Kuroki2007GraphicalIC,
  title={Graphical identifiability criteria for causal effects in studies with an unobserved treatment / response variable},
  author={Manabu Kuroki},
  year={2007}
}
We consider the problem of using data in studies with an unobserved treatment/response variable in order to evaluate average causal effects, when cause-effect relationships between variables can be described by a directed acyclic graph and the corresponding recursive factorization of a joint distribution. The paper proposes graphical criteria to test whether average causal effects are identifiable even if a treatment/response variable is unobserved. If the answer is affirmative, we provide… CONTINUE READING

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